WO2023005403A1 - 呼吸率检测方法、装置、存储介质及电子设备 - Google Patents

呼吸率检测方法、装置、存储介质及电子设备 Download PDF

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Publication number
WO2023005403A1
WO2023005403A1 PCT/CN2022/096219 CN2022096219W WO2023005403A1 WO 2023005403 A1 WO2023005403 A1 WO 2023005403A1 CN 2022096219 W CN2022096219 W CN 2022096219W WO 2023005403 A1 WO2023005403 A1 WO 2023005403A1
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area
target
target object
preset
breathing
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French (fr)
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覃德智
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Shanghai Sensetime Intelligent Technology Co Ltd
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Shanghai Sensetime Intelligent Technology Co Ltd
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Measuring devices for evaluating the respiratory organs
    • A61B5/0816—Measuring devices for examining respiratory frequency
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/01—Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G06F18/253—Fusion techniques of extracted features
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/60—Control of cameras or camera modules
    • H04N23/61—Control of cameras or camera modules based on recognised objects

Definitions

  • the present disclosure relates to the technical field of computer vision, and in particular to a breathing rate detection method, device, storage medium and electronic equipment.
  • the respiration rate is an important physiological parameter for analyzing the state of the human body.
  • Related technologies usually detect the respiration rate of the human body through a contact respiration rate detection device, such as a breathing belt or an electronic respiration rate measuring instrument.
  • a contact respiration rate detection device such as a breathing belt or an electronic respiration rate measuring instrument.
  • Contact-type respiration rate detection equipment cannot perform non-contact respiration rate detection on the human body. Therefore, it is difficult for related technologies to meet the demand for non-contact respiration rate detection.
  • the disclosure proposes a breathing rate detection method, device, storage medium and electronic equipment.
  • a breathing rate detection method which includes: acquiring at least two thermal images, the at least two thermal images are rendered based on temperature information in a preset area, and the preset area includes a target object, and the breathing area of the target object falls into the target area determined based on the preset area; for each of the at least two thermal images, extracting the temperature information of the target area in the thermal image, Wherein, the temperature information of the target object in the at least two thermal images changes periodically following the respiration of the target object; and the respiration rate of the target object is determined according to the extracted temperature information.
  • the respiration rate of the target object can be determined by analyzing the temperature information of the target area of the thermal image, so that the respiration rate detection result can be obtained without touching the target object, realizing non-contact detection and filling the gap It is blank in the contact detection scene, and has good detection speed and detection accuracy.
  • the respiration rate detection method is applied to a respiration rate detection device, and the respiration rate detection device includes a thermal imaging device, and the shooting area of the thermal imaging device is the preset area, and the method It also includes: acquiring a video stream, the frame images in the video stream are rendered based on the temperature information captured by the thermal imaging device; displaying the video stream, and marking the target area on the corresponding In the picture; said acquiring at least two thermal images includes: extracting at least two target frame images in the video stream as the at least two thermal images, and the at least two target frame images are the video frame images in the stream that meet a preset requirement, the preset requirement is that the target object enters the preset area and the breathing area of the target object falls within the target area of the at least two target frame images.
  • the frame image captured by the thermal imaging device can be displayed, and the target area can be marked in the display result, so that the target object can adjust its own posture and position according to the displayed picture, so as to ensure that the thermal image captured , its own breathing area falls into the target area, so as to ensure that the subsequent breathing rate based on the temperature information analysis of the target area is accurate.
  • the method before acquiring the video stream, further includes: monitoring the temperature state of the preset area; if the temperature state of the preset area changes, judging whether there is a target The object enters the preset area; in response to the fact that the target object enters the preset area, a video stream acquisition instruction is generated, and the video stream acquisition instruction is used to trigger execution of the operation of acquiring the video stream .
  • the method before acquiring the video stream, further includes: monitoring the preset area based on a preset sensor, and generating A video stream acquisition instruction, the video stream acquisition instruction is used to trigger execution of the operation of acquiring video stream, and the preset sensor includes a visual sensor or an inductive sensor.
  • the preset area can be monitored at low cost, and only when the target object enters the preset area, the above-mentioned thermal imaging device is triggered to start shooting and output a video stream, thereby minimizing the resources of the thermal imaging device consume.
  • the method further includes: triggering the execution of the operation of acquiring at least two thermal images in response to a breathing rate detection trigger instruction, where the breathing rate detection trigger instruction is used to indicate the preset requirement is met, the preset requirement is that the target object enters the preset area and the breathing area of the target object falls into the target area.
  • the activation frequency of the respiration rate detection can be reduced, thereby reducing the resource consumption of the respiration rate detection.
  • the method further includes: performing breathing area prediction on the frame images in the video stream based on a neural network to obtain a breathing area prediction result; combining the breathing area prediction result with the target area When the degree of overlap is higher than the preset threshold, the operation of acquiring at least two thermal images is triggered. Based on the above configuration, the activation frequency of the respiration rate detection can be reduced, thereby reducing the resource consumption of the respiration rate detection.
  • the neural network is obtained based on the following method: obtaining a sample thermal image set and labels corresponding to multiple sample thermal images in the sample thermal image set; wherein, the multiple sample thermal images are based on the sample The temperature information of the target object is rendered, and the label points to the breathing area of the sample target object; the breathing area is the mouth and nose area or the mask area; the multiple sample thermal images in the sample thermal image set are characterized Extracting to obtain a feature extraction result; predicting a breathing area according to the feature extraction result to obtain a breathing area prediction result; training the neural network according to the breathing area prediction result and the label. Based on the above configuration, the above neural network can be equipped with the ability to predict the breathing area.
  • the performing feature extraction on the multiple sample thermal images in the sample thermal image set to obtain a feature extraction result includes: for each sample thermal image, initializing the sample thermal image Feature extraction to obtain a first feature map; performing composite feature extraction on the first feature map to obtain first feature information, wherein the composite feature extraction includes channel feature extraction; based on the salient features in the first feature information, the Filtering the first feature map to obtain a filtering result; extracting second feature information from the filtering result; fusing the first feature information and the second feature information to obtain a feature extraction result of the sample thermal image.
  • information with sufficient discriminative power can be mined, the validity and discriminative power of the second feature information can be improved, and the richness of information in the final feature extraction result can be improved.
  • extracting the temperature information corresponding to the target area in the thermal image includes: for the target area in the thermal image, determining the temperature information corresponding to the pixel points in the target area; and calculating temperature information corresponding to the target area according to the temperature information corresponding to the pixel points. Based on the above configuration, by extracting the temperature information of the target area, the respiration rate of the target object can be further determined.
  • the determining the respiration rate of the target object according to the extracted temperature information includes: sorting the temperature information according to time sequence to obtain a temperature sequence; Noise reduction processing is performed to obtain a target temperature sequence; based on the target temperature sequence, the respiration rate of the target object is determined. Based on the above configuration, by determining the temperature sequence and performing noise reduction processing on the temperature sequence, the noise that affects the calculation of the respiration rate can be filtered out, so that the obtained respiration rate is more accurate.
  • the determining the respiration rate of the target subject based on the target temperature sequence includes: determining a plurality of key points in the target temperature sequence, and the key points are all peak points or mean points. is the valley point; for any two adjacent key points, determine the time interval between the two adjacent key points; according to the time interval, determine the breathing rate. Based on the above configuration, by calculating the time interval between adjacent key points, the respiration rate can be accurately determined.
  • a breathing rate detection device comprising: a thermal image acquisition module, configured to acquire at least two thermal images, the at least two thermal images are rendered based on temperature information of a preset area It is obtained that the target object is included in the preset area, and the breathing area of the target object falls into the target area determined based on the preset area; the temperature information extraction module is used for the at least two thermal images For each of the thermal images, the temperature information of the target area in the thermal image is extracted, wherein the temperature information of the target object in the at least two thermal images follows the breathing of the target object and presents periodic changes; the respiration rate is determined A module, configured to determine the respiration rate of the target object according to the extracted temperature information.
  • the breathing rate detection device includes a thermal imaging device, and the shooting area of the thermal imaging device is the preset area, and the device further includes a video stream processing module, configured to acquire a video stream, The frame image in the video stream is rendered based on the temperature information captured by the thermal imaging device; the video stream is displayed, and the target area is marked in the picture corresponding to the video stream; the thermal An image acquisition module, configured to extract at least two target frame images from the video stream as the at least two thermal images, and the at least two target frame images are frame images in the video stream that meet preset requirements , the preset requirement is that the target object enters the preset area and the breathing area of the target object falls into the target area of the at least two target frame images.
  • the device further includes a first video stream processing trigger module, configured to monitor the temperature state of the preset area; if the temperature state of the preset area changes, determine whether There is a situation that the target object enters the preset area; in response to the fact that the target object enters the preset area, a video stream acquisition instruction is generated, and the video stream acquisition instruction is used to trigger execution of the acquisition video stream operation.
  • a first video stream processing trigger module configured to monitor the temperature state of the preset area; if the temperature state of the preset area changes, determine whether There is a situation that the target object enters the preset area; in response to the fact that the target object enters the preset area, a video stream acquisition instruction is generated, and the video stream acquisition instruction is used to trigger execution of the acquisition video stream operation.
  • the device further includes a second video stream processing trigger module, configured to monitor the preset area based on preset sensors, and if the monitoring result indicates that there is a target object entering the preset area , generating a video stream acquisition instruction, where the video stream acquisition instruction is used to trigger execution of the operation of acquiring the video stream, and the preset sensor includes a visual sensor or an inductive sensor.
  • a second video stream processing trigger module configured to monitor the preset area based on preset sensors, and if the monitoring result indicates that there is a target object entering the preset area , generating a video stream acquisition instruction, where the video stream acquisition instruction is used to trigger execution of the operation of acquiring the video stream, and the preset sensor includes a visual sensor or an inductive sensor.
  • the device further includes a detection trigger module, configured to trigger the execution of the operation of acquiring at least two thermal images in response to a respiratory rate detection trigger instruction, the respiratory rate detection trigger instruction being used to indicate The preset requirement is met, the preset requirement is that the target object enters the preset area and the breathing area of the target object falls within the target area.
  • a detection trigger module configured to trigger the execution of the operation of acquiring at least two thermal images in response to a respiratory rate detection trigger instruction, the respiratory rate detection trigger instruction being used to indicate The preset requirement is met, the preset requirement is that the target object enters the preset area and the breathing area of the target object falls within the target area.
  • the device further includes a detection trigger module, configured to perform breathing area prediction on the frame images in the video stream based on a neural network to obtain a breathing area prediction result; when the breathing area prediction result is compared with When the overlapping degree of the target area is higher than a preset threshold, the operation of acquiring at least two thermal images is triggered.
  • a detection trigger module configured to perform breathing area prediction on the frame images in the video stream based on a neural network to obtain a breathing area prediction result; when the breathing area prediction result is compared with When the overlapping degree of the target area is higher than a preset threshold, the operation of acquiring at least two thermal images is triggered.
  • the neural network is obtained based on the following method: obtaining a sample thermal image set and labels corresponding to multiple sample thermal images in the sample thermal image set; wherein, the multiple sample thermal images are based on the sample The temperature information of the target object is rendered, and the label points to the breathing area of the sample target object; the breathing area is the mouth and nose area or the mask area; the multiple sample thermal images in the sample thermal image set are characterized Extracting to obtain a feature extraction result; predicting a breathing area according to the feature extraction result to obtain a breathing area prediction result; training the neural network according to the breathing area prediction result and the label.
  • the device includes a feature extraction module, configured to perform initial feature extraction on the sample thermal image for each sample thermal image to obtain a first feature map; perform composite feature extraction on the first feature map Extracting to obtain the first feature information, wherein the composite feature extraction includes channel feature extraction; based on the salient features in the first feature information, the first feature map is filtered to obtain a filtering result; the second filtering result is extracted Feature information: fusing the first feature information and the second feature information to obtain a feature extraction result of the thermal image of the sample.
  • a feature extraction module configured to perform initial feature extraction on the sample thermal image for each sample thermal image to obtain a first feature map; perform composite feature extraction on the first feature map Extracting to obtain the first feature information, wherein the composite feature extraction includes channel feature extraction; based on the salient features in the first feature information, the first feature map is filtered to obtain a filtering result; the second filtering result is extracted Feature information: fusing the first feature information and the second feature information to obtain a feature extraction result of the thermal image of the sample.
  • the temperature information extraction module is configured to, for the target area in the thermal image, determine temperature information corresponding to pixels in the target area; calculate Temperature information corresponding to the target area.
  • the respiration rate determination module is configured to sort the temperature information in time order to obtain a temperature sequence; perform noise reduction processing on the temperature sequence to obtain a target temperature sequence; based on the A target temperature sequence to determine the target subject's respiration rate.
  • the respiration rate determination module is configured to determine a plurality of key points in the target temperature sequence, and the key points are all peak points or valley points; for any two adjacent The key point is to determine the time interval between the two adjacent key points; according to the time interval, the respiration rate is determined.
  • an electronic device including at least one processor, and a memory communicatively connected to the at least one processor; wherein, the memory stores information executable by the at least one processor. instructions, the at least one processor implements the respiration rate detection method according to any one of the first aspect by executing the instructions stored in the memory.
  • a computer-readable storage medium is provided, at least one instruction or at least one program is stored in the computer-readable storage medium, and the at least one instruction or at least one program is loaded by a processor and Execute to realize the respiration rate detection method described in any one of the first aspect or the respiration rate detection method described in any one of the second aspect.
  • Fig. 1 shows a schematic flow chart of a breathing rate detection method according to an embodiment of the present disclosure
  • Fig. 2 shows a schematic diagram of a breathing rate detection scenario according to an embodiment of the present disclosure
  • Fig. 3 shows a schematic diagram of a target object posture position adjustment scene according to an embodiment of the present disclosure
  • FIG. 4 shows a schematic flow diagram of a feature extraction method according to an embodiment of the present disclosure
  • Fig. 5 shows a schematic flow chart of determining the respiration rate of a target object according to the extracted temperature information according to an embodiment of the present disclosure
  • Fig. 6 shows a schematic flow chart of determining a target subject's respiration rate based on extracted target temperature information according to an embodiment of the present disclosure
  • Fig. 7 shows a block diagram of a breathing rate detection device according to an embodiment of the present disclosure
  • Fig. 8 shows a block diagram of an electronic device according to an embodiment of the present disclosure
  • FIG. 9 shows a block diagram of another electronic device according to an embodiment of the present disclosure.
  • An embodiment of the present disclosure provides a breathing rate detection method, which can analyze the breathing rate of the subject based on the temperature change in the target area in the thermal image captured by the thermal imaging device, so that no direct contact with the subject is required
  • the respiration rate of the photographed subject can be obtained, thereby satisfying people's objective demand for non-contact measurement of the respiration rate.
  • the embodiments of the present disclosure may be used in various specific scenarios that require non-contact measurement of the respiration rate, and the embodiments of the present disclosure are not specifically limited to the specific scenarios.
  • the method provided by the embodiments of the present disclosure can be used to detect the non-contact breathing rate in scenes requiring isolation, in crowded scenes, in some public places with special requirements, and the like.
  • the respiration rate detection method provided by the embodiments of the present disclosure may be executed by a terminal device, a server or other types of electronic devices, wherein the terminal device may be a user equipment (User Equipment, UE), a mobile device, a user terminal, a cellular phone, a cordless phone , Personal Digital Assistant (PDA), handheld devices, computing devices, vehicle-mounted devices, wearable devices, etc.
  • the method for detecting the respiration rate may be implemented by the processor invoking computer-readable instructions stored in the memory. The method for detecting the respiration rate in the embodiment of the present disclosure will be described below by taking an electronic device as an execution body as an example.
  • Fig. 1 shows a schematic flow chart of a breathing rate detection method according to an embodiment of the present disclosure. As shown in Fig. 1, the above method includes:
  • S101 Acquire at least two thermal images, the thermal images are rendered based on temperature information of a preset area, the preset area includes a target object, and the breathing area of the target object falls into the target area determined based on the preset area .
  • the thermal image in the embodiments of the present disclosure may be obtained by imaging a thermal imaging device, and the shooting area of the thermal imaging device is the aforementioned preset area.
  • an area can be divided as the preset area, and the thermal imaging device is adjusted until the visual range of the thermal imaging device includes the preset area, for example, the visual range coincides with the preset area.
  • the embodiment of the present disclosure can acquire temperature information of each location point in the shooting area, and render a thermal image based on the temperature information.
  • the embodiment of the present disclosure detects the respiration rate according to the periodic variation of temperature in the thermal image, so at least two thermal images are required.
  • Embodiments of the present disclosure are intended to measure respiration rate, which is a physiological parameter, and the above-mentioned target object is a living body, such as a human being.
  • the embodiment of the present disclosure does not limit the control mode of the thermal imaging device, which may be triggered in response to a preset command, for example, a controller or a related sensor triggers a related control, and the thermal imaging device can start shooting.
  • the thermal imaging device may also be triggered in response to sensing information, for example, when the ambient temperature rises to a preset threshold, the thermal imaging device may automatically start taking pictures.
  • the thermal imaging device can also be triggered periodically.
  • the embodiment of the present disclosure does not limit the photographing mode of the thermal imaging device, for example, its photographing frame rate, photographing resolution mode, etc. can be set according to actual conditions.
  • the thermal imaging device can output the captured thermal image in the form of a video stream.
  • a video stream may be acquired, and frame images in the video stream are rendered based on temperature information captured by the thermal imaging device.
  • the video stream is displayed, and the target area is marked in a picture corresponding to the video stream.
  • FIG. 2 shows a schematic diagram of a respiration rate detection scenario according to an embodiment of the present disclosure.
  • the thermal imaging device can perform thermal imaging of the preset area 1, and output a corresponding video stream, and each frame of image in the video stream All are rendered based on the temperature information of the preset area 1.
  • a key area 2 can be determined based on the preset area 1, and when the target object enters the preset area 1 and the breathing area falls into the key area 2, the temperature change law of the key area 2 can reflect the breathing of the target object Rate.
  • the corresponding area of the key area 2 in the thermal image captured by the thermal imaging device can be uniquely determined, and this area is the above-mentioned target area 3 .
  • the target area 3 points to the area used to extract temperature change information in the frame image, that is to say, by extracting the temperature information of the target area 3 and analyzing its change law, the respiration rate of the target object can be determined.
  • the following uses a single target area as an example for illustration. The case of multiple target areas is based on the same inventive concept as the case of a single target area.
  • the above-mentioned video stream can be displayed, and the target area 3 can be marked on the display screen, so that the target object can adjust the posture position according to the display screen to ensure the breathing of the target object in the display screen.
  • the area falls into the target area 3
  • the attitude position in the present disclosure means attitude and/or position.
  • the shooting result of the target object by the thermal imaging device can be displayed, and the position corresponding to the target area 3 is also marked in the display screen, so that the target object can observe by itself.
  • FIG. 3 shows a schematic diagram of a target object pose position adjustment scenario according to an embodiment of the present disclosure.
  • the embodiment of the present disclosure considers that the change law of the temperature information corresponding to the target area 3 can accurately reflect the breathing rate of the target subject.
  • the embodiment of the present disclosure can extract at least two target frame images from the above video stream as at least two thermal images in step S101, the above target frame images are frame images in the above video stream that meet the preset requirements,
  • the preset requirement is that the target object enters the preset area and the breathing area of the target object falls into the target area of the target frame image.
  • the breathing area of the target object can be the mouth and nose area or the mask area.
  • the mouth and nose area can be understood as the mouth area and/or the nose area.
  • the mouth area and the nose area can be regarded as breathing area, or the mouth area and nasal area can be combined as one breathing area.
  • the frame image captured by the thermal imaging device can be displayed, and the target area can be marked in the display result, so that the target object can adjust its own posture and position according to the displayed picture, so as to ensure that the thermal image captured , its own breathing area falls into the target area, so as to ensure that the subsequent breathing rate based on the temperature information analysis of the target area is accurate.
  • the temperature state of the aforementioned preset area may also be monitored.
  • the temperature state of the preset area changes, it is determined whether the target object enters the preset area. If it exists, a video stream acquisition instruction is generated, and the video stream acquisition instruction is used to trigger execution of the above video stream acquisition operation.
  • the above-mentioned thermal imaging device can be triggered intermittently to monitor the temperature state of a preset area, and then only when the target object enters the preset area, the thermal imaging device outputs the above-mentioned video stream, and the target object has not yet entered the preset area.
  • the above video stream may not be output.
  • the intermittent shooting of the thermal imaging device can significantly reduce resource consumption compared to outputting the above video stream, that is to say, by multiplexing the thermal imaging device, the thermal imaging device can be started intermittently without the target object entering the preset area , and output the video stream when the target object is determined to enter the preset area, so as to achieve the purpose of reducing the resource consumption of the thermal imaging device to the greatest extent without additional hardware costs.
  • the preset area may also be monitored based on a preset sensor, and when the monitoring result indicates that a target object enters the preset area, a video stream acquisition instruction is generated, and the video stream acquisition instruction
  • the preset sensor includes a visual sensor or an inductive sensor.
  • the vision sensor which may be, for example, a color sensor or a grayscale sensor.
  • the inductive sensor for example, it may be an infrared sensor or a microwave sensor.
  • the preset area can be monitored at low cost, and only when the target object enters the preset area, the above-mentioned thermal imaging device is triggered to start shooting and output a video stream, thereby minimizing Resource consumption of thermal imaging devices.
  • the temperature analysis of the target area can accurately reflect the breathing rate only when the preset requirement is met.
  • the preset requirement can be expressed as the target object enters the preset area and the breathing area of the target object fall into the above target area.
  • step S101 may be triggered to be executed when a preset requirement is met. Based on the above configuration, the activation frequency of the respiration rate detection can be reduced, thereby reducing the resource consumption of the respiration rate detection.
  • the operation of acquiring at least two thermal images may be triggered in response to a breathing rate detection trigger instruction, where the breathing rate detection trigger instruction is used to indicate that the preset requirement is met.
  • the breathing rate detection trigger instruction is used to indicate that the preset requirement is met.
  • Embodiments of the present disclosure do not limit the triggering manner of the breathing rate detection triggering instruction, which may be triggered manually or by a machine. Taking the scene shown in Figure 3 as an example, after the target object enters the preset area, it can adjust its position and posture according to the display screen. When it determines that its own breathing area falls into the target area of the screen, it can trigger it by itself or inform others. The above respiration rate detection command.
  • the screen shown in FIG. 3 can also be displayed on the display interface (respiratory rate detection operator) of other users, and the respiration rate detection trigger instruction is triggered by the respiratory rate detection operator.
  • breathing area prediction may also be performed on frame images in the video stream based on a neural network to obtain a breathing area prediction result.
  • a preset threshold a preset threshold
  • the above operation of acquiring at least two thermal images is triggered.
  • Embodiments of the present disclosure do not limit the preset threshold, which can be set independently according to actual conditions. Based on this configuration, automatic respiration rate detection can be realized without manually controlling the start timing of the respiration rate detection method.
  • a neural network in the field of machine learning is a deep learning model that imitates the structure and function of biological neural networks.
  • Machine learning (Machine Learning, ML) is a multi-field interdisciplinary subject, involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. Specializes in the study of how computers simulate or implement human learning behaviors to acquire new knowledge or skills, and reorganize existing knowledge structures to continuously improve their performance.
  • Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent, and its application pervades all fields of artificial intelligence.
  • Machine learning and deep learning usually include techniques such as artificial neural network, belief network, reinforcement learning, transfer learning, inductive learning, and teaching learning.
  • Deep learning (Deep Learning, DL) is a branch of machine learning, which is an algorithm that attempts to perform high-level abstraction on data using multiple processing layers that contain complex structures or consist of multiple nonlinear transformations.
  • the sample thermal image set and the label corresponding to the sample image in the sample thermal image set can be obtained; the above sample thermal image is rendered based on the temperature information of the sample target object, and the above label points to the breathing area of the above sample target object; the above breathing area is Mouth and nose area or mask area; perform feature extraction on the thermal image of the above sample to obtain the feature extraction result; predict the breathing area according to the above feature extraction result, and obtain the prediction result of the breathing area; train the above neural network according to the above prediction result of the breathing area and the above label . Based on the above configuration, the trained neural network can be made capable of predicting the breathing area.
  • the embodiment of the present disclosure does not describe the above training process in detail.
  • the above neural network can perform feature extraction layer by layer based on the feature pyramid, predict the breathing area according to the extracted feature information, and adjust the parameters of the neural network according to the difference between the predicted breathing area and the above label. Since the sample thermal image is rendered based on temperature information, its clarity may be lower than that of the visible light image. In order to obtain sufficient discriminative feature information, the embodiments of the present disclosure optimize the feature extraction process.
  • FIG. 4 shows a schematic flowchart of a feature extraction method according to an embodiment of the present disclosure.
  • the above feature extraction includes:
  • the embodiment of the present disclosure does not limit the specific method of initial feature extraction.
  • at least one stage of convolution processing may be performed on the above image to obtain the above first feature map.
  • a plurality of image feature extraction results of different scales may be obtained, and at least two image feature extraction results of different scales may be fused to obtain the first feature map.
  • the above-mentioned performing composite feature extraction on the above-mentioned first feature map to obtain the first feature information may include: performing image feature extraction on the above-mentioned first feature map to obtain a first extraction result.
  • Channel information is extracted from the first feature map to obtain a second extraction result.
  • the above-mentioned first extraction result and the above-mentioned second extraction result are fused to obtain the above-mentioned first feature information.
  • the embodiment of the present disclosure does not limit the method for extracting image features from the above-mentioned first feature map. Exemplarily, it may perform at least one level of convolution processing on the above-mentioned first feature map to obtain the above-mentioned first extraction result.
  • the channel information extraction in the embodiment of the present disclosure can focus on the mining of the relationship between the channels in the first feature map. Exemplarily, it can be realized based on fusion of multi-channel features.
  • the composite feature extraction in the embodiment of the present disclosure can not only retain the low-level information of the first feature map itself, but also fully extract high-level inter-channel information by fusing the above-mentioned first extraction result and the above-mentioned second extraction result to improve mining.
  • the information richness and expressive power of the first feature information obtained.
  • at least one fusion method may be used, and the embodiment of the present disclosure does not limit the fusion method, at least one of dimensionality reduction, addition, multiplication, inner product, convolution, and averaging. Combinations can be used for fusion.
  • the salient feature may refer to signal information that is highly consistent with a heartbeat frequency of a living body (for example, a person) in the first feature information. Since the distribution of the salient features in the first feature information is relatively scattered, 70% of the information in the more salient area may be basically consistent with the heartbeat frequency, and the less salient area actually includes salient features.
  • the embodiment of the present disclosure does not limit the salient feature judgment method, which may be based on a neural network or based on expert experience.
  • the above-mentioned suppressing the above-mentioned salient features in the filtering results to obtain the second feature map includes: performing feature extraction on the above-mentioned filtering results to obtain target features, and the above-mentioned The target feature is extracted by performing composite feature extraction to obtain target feature information, and based on the salient features in the target feature information, the target feature is filtered to obtain the above second feature map.
  • the stop condition is that the proportion of the salient features in the second feature map is less than 5%, and for example, the stop condition is that the number of updates of the second feature map reaches the preset number of times
  • the stop condition is that the number of updates of the second feature map reaches the preset number of times
  • the salient features can be filtered layer by layer based on the hierarchical structure, and compound feature extraction including channel information extraction can be performed based on the filtering results to obtain the second feature information including multiple target feature information, and discriminative information can be mined layer by layer , improve the validity and discriminative power of the second feature information, and then improve the richness of information in the final feature extraction result.
  • the feature extraction method in the embodiments of the present disclosure can be used to perform feature extraction on the sample thermal image, and can be used in each of the embodiments of the present disclosure when it is necessary to train a neural network based on the sample thermal image.
  • the corresponding position of the key area in the preset area in the thermal image can be determined as the preset area, and the preset area is only related to the position of the key area, and has nothing to do with the position information of each pixel in the thermal image , the target area is uniquely determined according to the preset area.
  • the temperature information corresponding to the relevant pixel points in the above target area can be determined; according to the temperature information corresponding to each of the above relevant pixel points, the temperature corresponding to the above target area can be calculated information.
  • the respiration rate of the target object can be further determined.
  • each pixel in the target area may be the relevant pixel.
  • pixel filtering can also be performed based on the temperature information of each pixel in the target area, and the pixels whose temperature information does not meet the preset temperature requirements are filtered out, and the unfiltered pixels are determined as the relevant pixel.
  • Embodiments of the present disclosure do not limit the preset temperature requirement, for example, an upper temperature limit, a lower temperature limit or a temperature range may be defined.
  • the embodiment of the present disclosure does not limit the specific method for calculating the temperature information corresponding to the target area.
  • the mean value or weighted mean value of the temperature information corresponding to each relevant pixel point can be determined as the temperature information corresponding to the target area.
  • the embodiment of the present disclosure does not limit the weight value, which can be set by the user according to actual needs.
  • the weight value may be anti-correlated with the distance between the corresponding relevant pixel point and the center position of the target area. Exemplarily, if the relevant pixel is closer to the center of the target area, the weight is higher, and if the relevant pixel is farther from the center of the target, the weight is lower.
  • the embodiment of the present disclosure considers that when the breathing area of the target object falls into the target area, the breathing of the target object will cause the temperature of the target area to show a periodic change pattern.
  • the target object inhales
  • the temperature of the target area will follow.
  • the target object exhales
  • the temperature of the target area will increase accordingly, and the breathing rate of the target object can be determined by analyzing the periodic change rule of the extracted temperature information.
  • FIG. 5 shows a schematic flowchart of determining the respiration rate of the target object according to the extracted temperature information according to an embodiment of the present disclosure, including:
  • the thermal images can be sorted according to the time sequence of the acquired thermal images to obtain a thermal image sequence, and the temperature information of the target area in each thermal image can be extracted to obtain the temperature sequence.
  • each thermal image includes the target object A
  • a temperature sequence containing 200 pieces of temperature information can be obtained, and the change law of the temperature information of the temperature sequence reflects the breathing of the target object A. Rate.
  • the embodiments of the present disclosure can simultaneously detect the respiration rate of multiple target objects, and only need to ensure that the breathing area of each target object falls into its unique corresponding target area.
  • the above operations may be performed for each target area to obtain a corresponding temperature sequence, and then determine the respiration rate of the target object corresponding to the target area.
  • a noise reduction processing strategy and a noise reduction processing method may be determined; according to the above noise reduction processing strategy and based on the above noise reduction method, the above temperature sequence is processed to obtain the above target temperature sequence.
  • noise reduction processing strategies include at least one of the following: noise reduction based on high-frequency threshold, noise reduction based on low-frequency threshold, random noise filtering, and posterior noise reduction.
  • the above noise reduction processing is implemented based on at least one of the following manners: independent component analysis, Laplacian pyramid, bandpass filtering, wavelet, and Hamming window.
  • the respiration rate verification conditions and noise reduction experience parameters corresponding to the posterior noise reduction you can set the respiration rate verification conditions and noise reduction experience parameters corresponding to the posterior noise reduction, and denoise the above temperature sequence according to the noise reduction experience parameters to obtain the target temperature sequence.
  • the embodiment of the present disclosure does not limit the method for determining the noise reduction experience parameter, which may be obtained according to expert experience.
  • FIG. 6 shows a schematic flowchart of determining the respiration rate of a target object based on the extracted target temperature information according to an embodiment of the present disclosure, including:
  • the corresponding time intervals can be calculated for every two adjacent key points, and then N-1 time intervals can be determined.
  • Embodiments of the present disclosure do not limit the specific method for determining the above-mentioned respiration rate according to the time interval.
  • the reciprocal of one of them can be determined as the above-mentioned respiration rate, and the respiration rate can also be determined based on some or all of the time intervals, for example, the above-mentioned several time intervals or all
  • the reciprocal of the mean value of the time interval was determined as the above-mentioned respiration rate.
  • the embodiments of the present disclosure can accurately determine the respiration rate by calculating the time interval between adjacent key points.
  • the respiration rate detection method can determine the respiration rate of the target object by analyzing the temperature information of the target area when the target object enters a preset area and its breathing area falls into the target area.
  • the whole process does not require contact with the target object and can be widely used in various scenarios.
  • patients can monitor the patient's breathing rate without wearing any equipment, reduce the patient's discomfort, and improve the quality, effectiveness and efficiency of patient monitoring.
  • a closed scene such as an office or the lobby of an office building, the breathing rate of the people present is detected to determine whether there is any abnormality.
  • the baby's breathing can be detected to prevent the baby from suffocating due to food blocking the airway, and the baby's breathing rate can be analyzed in real time to judge the baby's health status.
  • remote-controlled thermal imaging equipment can be used to shoot targets that may become the source of infection, and monitor the vital signs of the target while avoiding infection.
  • the respiration rate detection method provided by the embodiments of the present disclosure can determine the respiration rate of the target object by analyzing the temperature information of the target area of the thermal image captured by the thermal imaging device, so as to obtain the respiration rate without touching the target object. High-rate detection results, realize non-contact detection, fill the blank of non-contact detection scene, and have good detection speed and detection accuracy.
  • Fig. 7 shows a block diagram of a breathing rate detection device according to an embodiment of the present disclosure. As shown in Figure 7, the above-mentioned devices include:
  • the thermal image acquisition module 10 is configured to acquire at least two thermal images, the at least two thermal images are rendered based on the temperature information of a preset area, the preset area includes a target object, and the breathing area of the target object falls within a range based on In the target area determined by the above preset area;
  • the temperature information extraction module 20 is configured to extract the temperature information of the target area in the thermal image for each of the at least two thermal images, wherein the temperature information of the target object in the at least two thermal images follows The respiration of the above-mentioned subject exhibits periodic changes;
  • the respiration rate determination module 30 is configured to determine the respiration rate of the target object according to the extracted temperature information.
  • the breathing rate detection device includes a thermal imaging device, and the shooting area of the thermal imaging device is the preset area, and the device further includes a video stream processing module, configured to obtain a video stream, in which The frame image is rendered based on the temperature information captured by the above-mentioned thermal imaging device; the above-mentioned video stream is displayed, and the above-mentioned target area is marked in the picture corresponding to the above-mentioned video stream; the above-mentioned thermal image acquisition module is used for the above-mentioned video stream At least two target frame images are extracted from the above-mentioned at least two thermal images, and the above-mentioned at least two target frame images are frame images in the above-mentioned video stream that meet the preset requirements, and the above-mentioned preset requirements are that the above-mentioned target object enters the above-mentioned preset area and The breathing area of the target object falls into the target area of the at least two target frame images.
  • the above-mentioned device further includes a first video stream processing trigger module, configured to monitor the temperature state of the above-mentioned preset area; when the temperature state of the above-mentioned preset area changes, determine whether there is a target object The case of entering the preset area; in response to the fact that the target object enters the preset area, a video stream acquisition instruction is generated, and the video stream acquisition instruction is used to trigger the execution of the above video stream acquisition operation.
  • a first video stream processing trigger module configured to monitor the temperature state of the above-mentioned preset area; when the temperature state of the above-mentioned preset area changes, determine whether there is a target object The case of entering the preset area; in response to the fact that the target object enters the preset area, a video stream acquisition instruction is generated, and the video stream acquisition instruction is used to trigger the execution of the above video stream acquisition operation.
  • the above-mentioned device further includes a second video stream processing trigger module, configured to monitor the above-mentioned preset area based on a preset sensor, and generate a video if the monitoring result indicates that there is a target object entering the above-mentioned preset area.
  • a stream acquisition instruction, the above-mentioned video stream acquisition instruction is used to trigger the execution of the above-mentioned operation of acquiring the video stream, and the above-mentioned preset sensor includes a visual sensor or an inductive sensor.
  • the above device further includes a detection trigger module, configured to trigger the execution of the above operation of acquiring at least two thermal images in response to a respiration rate detection trigger instruction, and the respiration rate detection trigger instruction is used to indicate the above preset The requirement is met, and the preset requirement is that the target object enters the preset area and the breathing area of the target object falls into the target area.
  • a detection trigger module configured to trigger the execution of the above operation of acquiring at least two thermal images in response to a respiration rate detection trigger instruction, and the respiration rate detection trigger instruction is used to indicate the above preset The requirement is met, and the preset requirement is that the target object enters the preset area and the breathing area of the target object falls into the target area.
  • the above-mentioned device further includes a detection trigger module, which is used to predict the breathing area of the frame images in the above-mentioned video stream based on a neural network to obtain a breathing area prediction result; when the above-mentioned breathing area prediction result and the above-mentioned target area When the coincidence degree is higher than the preset threshold, the above operation of acquiring at least two thermal images is triggered.
  • a detection trigger module which is used to predict the breathing area of the frame images in the above-mentioned video stream based on a neural network to obtain a breathing area prediction result; when the above-mentioned breathing area prediction result and the above-mentioned target area
  • the coincidence degree is higher than the preset threshold
  • the above-mentioned neural network obtains the labels corresponding to the sample thermal image set and the multiple sample thermal images in the above-mentioned sample thermal image set based on the following method; wherein, the above-mentioned multiple sample thermal images are based on the temperature of the sample target object
  • the information is rendered, and the above-mentioned label points to the breathing area of the above-mentioned sample target object; the above-mentioned breathing area is the mouth and nose area or the mask area; the feature extraction is performed on the multiple sample thermal images in the above-mentioned sample thermal image set, and the feature extraction result is obtained; according to The above feature extraction result predicts the breathing area, and obtains the breathing area prediction result; according to the above breathing area prediction result and the above label, the above neural network is trained.
  • the above-mentioned device includes a feature extraction module, which is used to perform initial feature extraction on the sample thermal image for each sample thermal image to obtain a first feature map; perform composite feature extraction on the first feature map , to obtain the first feature information, wherein the composite feature extraction includes channel feature extraction; based on the salient features in the first feature information, the first feature map is filtered to obtain a filtering result; the second feature in the filtering result is extracted information; fusing the first feature information and the second feature information to obtain a feature extraction result of the thermal image of the sample.
  • a feature extraction module which is used to perform initial feature extraction on the sample thermal image for each sample thermal image to obtain a first feature map; perform composite feature extraction on the first feature map , to obtain the first feature information, wherein the composite feature extraction includes channel feature extraction; based on the salient features in the first feature information, the first feature map is filtered to obtain a filtering result; the second feature in the filtering result is extracted information; fusing the first feature information and the second feature information to obtain
  • the above-mentioned temperature information extraction module is configured to, for the target area in the thermal image, determine the temperature information corresponding to the pixels in the above-mentioned target area; calculate the above-mentioned target area according to the temperature information corresponding to the above-mentioned pixel points Corresponding temperature information.
  • the respiration rate determination module is configured to sort the above temperature information in time order to obtain a temperature sequence; perform noise reduction processing on the above temperature sequence to obtain a target temperature sequence; based on the above target temperature sequence, Determine the respiration rate of the aforementioned target subject.
  • the respiration rate determination module is configured to determine multiple key points in the target temperature sequence, and the key points are all peak points or valley points; for any two adjacent key points, The time interval between the above two adjacent key points is determined; according to the above time interval, the above breathing rate is determined.
  • the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the methods described in the method embodiments above, and its specific implementation can refer to the description of the method embodiments above. For brevity, here No longer.
  • Embodiments of the present disclosure also provide a computer-readable storage medium, wherein at least one instruction or at least one program is stored in the computer-readable storage medium, and the above-mentioned method is implemented when the at least one instruction or at least one program is loaded and executed by a processor.
  • the computer readable storage medium may be a non-transitory computer readable storage medium.
  • An embodiment of the present disclosure also proposes an electronic device, including: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured as the above method.
  • Electronic devices may be provided as terminals, servers, or other forms of devices.
  • Fig. 8 shows a block diagram of an electronic device according to an embodiment of the present disclosure.
  • the electronic device 800 may be a terminal such as a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, or a personal digital assistant.
  • electronic device 800 may include one or more of the following components: processing component 802, memory 804, power supply component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814 , and the communication component 816.
  • the processing component 802 generally controls the overall operations of the electronic device 800, such as those associated with display, telephone calls, data communications, camera operations, and recording operations.
  • the processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above method. Additionally, processing component 802 may include one or more modules that facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802 .
  • the memory 804 is configured to store various types of data to support operations at the electronic device 800 . Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and the like.
  • the memory 804 can be implemented by any type of volatile or non-volatile storage device or their combination, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic or Optical Disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read-only memory
  • EPROM erasable Programmable Read Only Memory
  • PROM Programmable Read Only Memory
  • ROM Read Only Memory
  • Magnetic Memory Flash Memory
  • Magnetic or Optical Disk Magnetic Disk
  • the power supply component 806 provides power to various components of the electronic device 800 .
  • Power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for electronic device 800 .
  • the multimedia component 808 includes a screen providing an output interface between the above-mentioned electronic device 800 and the user.
  • the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user.
  • the touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel.
  • the above-mentioned touch sensor may not only sense a boundary of a touch or a sliding action, but also detect a duration and pressure related to the above-mentioned touching or sliding operation.
  • the multimedia component 808 includes a front camera and/or a rear camera. When the electronic device 800 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera can receive external multimedia data.
  • Each front camera and rear camera can be a fixed optical lens system or have focal length and optical zoom capability.
  • the audio component 810 is configured to output and/or input audio signals.
  • the audio component 810 includes a microphone (MIC), which is configured to receive external audio signals when the electronic device 800 is in operation modes, such as call mode, recording mode and voice recognition mode. Received audio signals may be further stored in memory 804 or sent via communication component 816 .
  • the audio component 810 also includes a speaker for outputting audio signals.
  • the I/O interface 812 provides an interface between the processing component 802 and a peripheral interface module, which may be a keyboard, a click wheel, a button, and the like. These buttons may include, but are not limited to: a home button, volume buttons, start button, and lock button.
  • Sensor assembly 814 includes one or more sensors for providing status assessments of various aspects of electronic device 800 .
  • the sensor component 814 can detect the open/close state of the electronic device 800, the relative positioning of the components, such as the above-mentioned components are the display and the keypad of the electronic device 800, the sensor component 814 can also detect the electronic device 800 or a component of the electronic device 800 Changes in the position of , presence or absence of user contact with the electronic device 800 , orientation or acceleration/deceleration of the electronic device 800 and temperature changes of the electronic device 800 .
  • Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects in the absence of any physical contact.
  • Sensor assembly 814 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications.
  • the sensor component 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor or a temperature sensor.
  • the communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices.
  • the electronic device 800 can access wireless networks based on communication standards, such as WiFi, 2G, 3G, 4G, 5G or combinations thereof.
  • the communication component 816 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel.
  • the aforementioned communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication.
  • the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, Infrared Data Association (IrDA) technology, Ultra Wide Band (UWB) technology, Bluetooth (BT) technology and other technologies.
  • RFID Radio Frequency Identification
  • IrDA Infrared Data Association
  • UWB Ultra Wide Band
  • Bluetooth Bluetooth
  • electronic device 800 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable A programmable gate array (FPGA), controller, microcontroller, microprocessor or other electronic component implementation for performing the methods described above.
  • ASICs application specific integrated circuits
  • DSPs digital signal processors
  • DSPDs digital signal processing devices
  • PLDs programmable logic devices
  • FPGA field programmable A programmable gate array
  • controller microcontroller, microprocessor or other electronic component implementation for performing the methods described above.
  • a non-volatile computer-readable storage medium such as the memory 804 including computer program instructions, which can be executed by the processor 820 of the electronic device 800 to implement the above method.
  • FIG. 9 shows a block diagram of another electronic device according to an embodiment of the present disclosure.
  • electronic device 1900 may be provided as a server.
  • electronic device 1900 includes processing component 1922 , which further includes one or more processors, and a memory resource represented by memory 1932 for storing instructions executable by processing component 1922 , such as application programs.
  • the application programs stored in memory 1932 may include one or more modules each corresponding to a set of instructions.
  • the processing component 1922 is configured to execute instructions to perform the above method.
  • Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input-output (I/O) interface 1958 .
  • the electronic device 1900 can operate based on an operating system stored in the memory 1932, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM or the like.
  • a non-transitory computer-readable storage medium such as the memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to implement the above method.
  • the present disclosure can be a system, method and/or computer program product.
  • a computer program product may include a computer readable storage medium having computer readable program instructions thereon for causing a processor to implement various aspects of the present disclosure.
  • a computer readable storage medium may be a tangible device that can retain and store instructions for use by an instruction execution device.
  • a computer readable storage medium may be, for example, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
  • Computer-readable storage media include: portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or flash memory), static random access memory (SRAM), compact disc read only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanically encoded device, such as a printer with instructions stored thereon A hole card or a raised structure in a groove, and any suitable combination of the above.
  • RAM random access memory
  • ROM read-only memory
  • EPROM erasable programmable read-only memory
  • flash memory static random access memory
  • SRAM static random access memory
  • CD-ROM compact disc read only memory
  • DVD digital versatile disc
  • memory stick floppy disk
  • mechanically encoded device such as a printer with instructions stored thereon
  • a hole card or a raised structure in a groove and any suitable combination of the above.
  • computer-readable storage media are not to be construed as transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., pulses of light through fiber optic cables), or transmitted electrical signals.
  • Computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or downloaded to an external computer or external storage device over a network, such as the Internet, a local area network, a wide area network, and/or a wireless network.
  • the network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers.
  • a network adapter card or a network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing/processing device .
  • Computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or Source or object code written in any combination of the above programming languages including object-oriented programming languages—such as Smalltalk, C++, etc., and conventional procedural programming languages—such as “C” or similar programming languages.
  • Computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server implement.
  • the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as via the Internet using an Internet service provider). connect).
  • LAN local area network
  • WAN wide area network
  • an electronic circuit such as a programmable logic circuit, field programmable gate array (FPGA), or programmable logic array (PLA)
  • FPGA field programmable gate array
  • PDA programmable logic array
  • These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine such that when executed by the processor of the computer or other programmable data processing apparatus , producing an apparatus for realizing the functions/actions specified in one or more blocks in the flowchart and/or block diagram.
  • These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause computers, programmable data processing devices and/or other devices to work in a specific way, so that the computer-readable medium storing instructions includes An article of manufacture comprising instructions for implementing various aspects of the functions/acts specified in one or more blocks in flowcharts and/or block diagrams.
  • each block in a flowchart or block diagram may represent a module, a portion of a program segment, or an instruction that includes one or more programmable logic components for implementing specified logical functions.
  • Execute instructions may be executed.
  • the order noted in the blocks may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations can be implemented by a dedicated hardware-based system that performs the specified function or action , or may be implemented by a combination of dedicated hardware and computer instructions.

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Abstract

一种呼吸率检测方法、装置、存储介质及电子设备。方法包括获取至少两张热图像，至少两张热图像基于预设区域的温度信息渲染得到，预设区域内包括目标对象，并且目标对象的呼吸区域落入基于预设区域确定的目标区域中（S101）；对于至少两张热图像中的每一张，提取热图像中目标区域的温度信息，至少两张热图像中目标对象的温度信息跟随目标对象的呼吸呈现周期性变化（S102）；根据提取到的温度信息，确定目标对象的呼吸率（S103）。通过对热图像的目标区域的温度信息进行分析，可以确定出目标对象的呼吸率，从而实现了非接触检测，并且具备良好的检测速度和检测准确度。

Description

呼吸率检测方法、装置、存储介质及电子设备
相关申请的交叉引用
本专利申请要求于2021年7月30日提交的、申请号为202110871818.0的中国专利申请的优先权，该申请的全文以引用的方式并入本文中。
技术领域
本公开涉及计算机视觉技术领域，尤其涉及呼吸率检测方法、装置、存储介质及电子设备。
背景技术
呼吸率是进行人体状态分析的重要生理参数，相关技术通常通过接触式呼吸率检测设备对人体进行呼吸率检测，比如，呼吸带或者电子式呼吸率测量仪等。接触式呼吸率检测设备无法对人体进行非接触式的呼吸率检测，因此，相关技术难以满足对于无接触呼吸率检测的需求。
发明内容
本公开提出了呼吸率检测方法、装置、存储介质及电子设备。
根据本公开的一方面，提供了一种呼吸率检测方法，其包括：获取至少两张热图像，所述至少两张热图像基于预设区域的温度信息渲染得到，所述预设区域内包括目标对象，并且所述目标对象的呼吸区域落入基于所述预设区域确定的目标区域中；对于所述至少两张热图像中的每一张，提取该热图像中目标区域的温度信息，其中，所述至少两张热图像中所述目标对象的温度信息跟随所述目标对象的呼吸呈现周期性变化；根据提取到的温度信息，确定所述目标对象的呼吸率。基于上述配置，通过对热图像的目标区域的温度信息进行分析，可以确定出目标对象的呼吸率，从而在不接触目标对象的情况下得到呼吸率检测结果，实现了非接触检测，填补了非接触检测场景的空白，并且具备良好的检测速度和检测准确度。
在一些可能的实施方式中，所述呼吸率检测方法应用于呼吸率检测装置，所述呼吸率检测装置包括热成像设备，所述热成像设备的拍摄区域为所述预设区域，所述方法还包括：获取视频流，所述视频流中的帧图像基于所述热成像设备拍摄到的温度信息渲染得到；对所述视频流进行显示，并且将所述目标区域标记在所述视频流对应的画面中；所述获取至少两个热图像，包括：在所述视频流中提取至少两张目标帧图像，作为所述至少两张热图像，所述至少两张目标帧图像为所述视频流中满足预设要求的帧图像，所述预设要求为所述目标对象进入所述预设区域并且所述目标对象的呼吸区域落入所述至少两张目标帧图像的目标区域。基于上述配置，可以对热成像设备拍摄到的帧图像进行显示，并在显示结果中标记目标区域，可以使得目标对象能够根据显示的画面配合调 整自身姿态位置，以确保在获取到的热图像中，其自身呼吸区域落入目标区域，从而确保后续基于目标区域的温度信息分析得到的呼吸率准确。
在一些可能的实施方式中，所述获取视频流之前，所述方法还包括：监控所述预设区域的温度状态；在所述预设区域的温度状态发生变化的情况下，判断是否存在目标对象进入所述预设区域的情况；响应于存在所述目标对象进入所述预设区域的情况，则生成视频流获取指令，所述视频流获取指令用于触发执行所述获取视频流的操作。基于上述配置，可以通过复用热成像设备，达到在无需额外增加硬件成本的前提下，较大限度降低热成像设备的资源消耗的目的。
在一些可能的实施方式中，所述获取视频流之前，所述方法还包括：基于预置传感器监控所述预设区域，在监控结果指示存在目标对象进入所述预设区域的情况下，生成视频流获取指令，所述视频流获取指令用于触发执行所述获取视频流的操作，所述预置传感器包括视觉传感器或感应传感器。基于上述配置，可以低成本对预设区域进行监控，并且只有在目标对象进入预设区域的情况下，才触发上述热成像设备启动拍摄，并输出视频流，从而最大限度降低热成像设备的资源消耗。
在一些可能的实施方式中，所述方法还包括：响应于呼吸率检测触发指令，触发执行所述获取至少两张热图像的操作，所述呼吸率检测触发指令用于指示所述预设要求被满足，所述预设要求为所述目标对象进入所述预设区域并且所述目标对象的呼吸区域落入所述目标区域。基于上述配置，可以降低呼吸率检测的启动频率，从而降低呼吸率检测的资源消耗。
在一些可能的实施方式中，所述方法还包括：基于神经网络对所述视频流中的帧图像进行呼吸区域预测，得到呼吸区域预测结果；在所述呼吸区域预测结果与所述目标区域的重合度高于预设阈值的情况下，触发执行所述获取至少两张热图像的操作。基于上述配置，可以降低呼吸率检测的启动频率，从而降低呼吸率检测的资源消耗。
在一些可能的实施方式中，所述神经网络基于下述方法得到：获取样本热图像集和所述样本热图像集中多张样本热图像对应的标签；其中，所述多张样本热图像基于样本目标对象的温度信息渲染得到，所述标签指向所述样本目标对象的呼吸区域；所述呼吸区域为口鼻区域或口罩区域；对所述样本热图像集中的所述多张样本热图像进行特征提取，得到特征提取结果；根据所述特征提取结果预测呼吸区域，得到呼吸区域预测结果；根据所述呼吸区域预测结果和所述标签，训练所述神经网络。基于上述配置，可以使得上述神经网络具备预测呼吸区域的能力。
在一些可能的实施方式中，所述对所述样本热图像集中的所述多张样本热图像进行特征提取，得到特征提取结果，包括：针对每张样本热图像，对该样本热图像进行初始特征提取，得到第一特征图；对该第一特征图进行复合特征提取，得到第一特征信息，其中，该复合特征提取包括通道特征提取；基于该第一特征信息中的显著特征，对该第一特征图进行过滤得到过滤结果；提取该过滤结果中的第二特征信息；融合该第一特征信息和该第二特征信息，得到该样本热图像的特征提取结果。基于上述配置，可以挖掘充分具有判别力的信息，提升第二特征信息的有效度和判别力，进而提升最终的特征提取结果中信息的丰富程度。
在一些可能的实施方式中，所述对于所述至少两张热图像中的每一张，提取该热图像中的目标区域对应的温度信息，包括：对于该热图像中的目标区域，确定所述目标区域中像素点对应的温度信息；根据所述像素点对应的温度信息，计算所述目标区域对应的温度信息。基于上述配置，通过提取目标区域的温度信息，可以进而确定目标对象的呼吸率。
在一些可能的实施方式中，所述根据提取到的所述温度信息，确定所述目标对象的呼吸率，包括：按照时间顺序对所述温度信息进行排序，得到温度序列；对所述温度序列进行降噪处理，得到目标温度序列；基于所述目标温度序列，确定所述目标对象的呼吸率。基于上述配置，通过确定温度序列，对该温度序列进行降噪处理，可以滤除影响呼吸率计算的噪声，使得得到的呼吸率更为准确。
在一些可能的实施方式中，所述基于所述目标温度序列，确定所述目标对象的呼吸率，包括：确定所述目标温度序列中多个关键点，所述关键点均为峰值点或均为谷值点；对于任意两个相邻关键点，确定所述两个相邻关键点之间时间间隔；根据所述时间间隔，确定所述呼吸率。基于上述配置，通过计算相邻关键点之间的时间间隔，可以准确地确定呼吸率。
根据本公开的第二方面，提供一种呼吸率检测装置，所述装置包括：热图像获取模块，用于获取至少两张热图像，所述至少两张热图像基于预设区域的温度信息渲染得到，所述预设区域内包括目标对象，并且所述目标对象的呼吸区域落入基于所述预设区域确定的目标区域中；温度信息提取模块，用于对于所述至少两张热图像中的每一张，提取该热图像中所述目标区域的温度信息，其中，所述至少两张热图像中所述目标对象的温度信息跟随所述目标对象的呼吸呈现周期性变化；呼吸率确定模块，用于根据提取到的温度信息，确定所述目标对象的呼吸率。
在一些可能的实施方式中，所述呼吸率检测装置包括热成像设备，所述热成像设备的拍摄区域为所述预设区域，所述装置还包括视频流处理模块，用于获取视频流，所述视频流中的帧图像基于所述热成像设备拍摄到的温度信息渲染得到；对所述视频流进行显示，并且将所述目标区域标记在所述视频流对应的画面中；所述热图像获取模块，用于在所述视频流中提取至少两张目标帧图像，作为所述至少两张热图像，所述至少两张目标帧图像为所述视频流中满足预设要求的帧图像，所述预设要求为所述目标对象进入所述预设区域并且所述目标对象的呼吸区域落入所述至少两张目标帧图像的目标区域。
在一些可能的实施方式中，所述装置还包括第一视频流处理触发模块，用于监控所述预设区域的温度状态；在所述预设区域的温度状态发生变化的情况下，判断是否存在目标对象进入所述预设区域的情况；响应于存在所述目标对象进入所述预设区域的情况，则生成视频流获取指令，所述视频流获取指令用于触发执行所述获取视频流的操作。
在一些可能的实施方式中，所述装置还包括第二视频流处理触发模块，用于基于预置传感器监控所述预设区域，在监控结果指示存在目标对象进入所述预设区域的情况下，生成视频流获取指令，所述视频流获取指令用于触发执行所述获取视频流的操作，所述预置传感器包括视觉传感器或感应传感器。
在一些可能的实施方式中，所述装置还包括检测触发模块，用于响应于呼吸率检测 触发指令，触发执行所述获取至少两张热图像的操作，所述呼吸率检测触发指令用于指示所述预设要求被满足，所述预设要求为所述目标对象进入所述预设区域并且所述目标对象的呼吸区域落入所述目标区域。
在一些可能的实施方式中，所述装置还包括检测触发模块，用于基于神经网络对所述视频流中的帧图像进行呼吸区域预测，得到呼吸区域预测结果；在所述呼吸区域预测结果与所述目标区域的重合度高于预设阈值的情况下，触发执行所述获取至少两张热图像的操作。
在一些可能的实施方式中，所述神经网络基于下述方法得到：获取样本热图像集和所述样本热图像集中多张样本热图像对应的标签；其中，所述多张样本热图像基于样本目标对象的温度信息渲染得到，所述标签指向所述样本目标对象的呼吸区域；所述呼吸区域为口鼻区域或口罩区域；对所述样本热图像集中的所述多张样本热图像进行特征提取，得到特征提取结果；根据所述特征提取结果预测呼吸区域，得到呼吸区域预测结果；根据所述呼吸区域预测结果和所述标签，训练所述神经网络。
在一些可能的实施方式中，所述装置包括特征提取模块，用于针对每张样本热图像，对该样本热图像进行初始特征提取，得到第一特征图；对该第一特征图进行复合特征提取，得到第一特征信息，其中，该复合特征提取包括通道特征提取；基于该第一特征信息中的显著特征，对该第一特征图进行过滤得到过滤结果；提取该过滤结果中的第二特征信息；融合该第一特征信息和该第二特征信息，得到该样本热图像的特征提取结果。
在一些可能的实施方式中，所述温度信息提取模块，用于对于该热图像中的目标区域，确定所述目标区域中像素点对应的温度信息；根据所述像素点对应的温度信息，计算所述目标区域对应的温度信息。
在一些可能的实施方式中，所述呼吸率确定模块，用于按照时间顺序对所述温度信息进行排序，得到温度序列；对所述温度序列进行降噪处理，得到目标温度序列；基于所述目标温度序列，确定所述目标对象的呼吸率。
在一些可能的实施方式中，所述呼吸率确定模块，用于确定所述目标温度序列中多个关键点，所述关键点均为峰值点或均为谷值点；对于任意两个相邻关键点，确定所述两个相邻关键点之间时间间隔；根据所述时间间隔，确定所述呼吸率。
根据本公开的第三方面，提供了一种电子设备，包括至少一个处理器，以及与所述至少一个处理器通信连接的存储器；其中，所述存储器存储有可被所述至少一个处理器执行的指令，所述至少一个处理器通过执行所述存储器存储的指令实现如第一方面中任意一项所述的呼吸率检测方法。
根据本公开的第四方面，提供了一种计算机可读存储介质，所述计算机可读存储介质中存储有至少一条指令或至少一段程序，所述至少一条指令或至少一段程序由处理器加载并执行以实现如第一方面中任意一项所述的呼吸率检测方法或第二方面中任意一项所述的呼吸率检测方法。
应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，而非限制本公开。
根据下面参考附图对示例性实施例的详细说明，本公开的其它特征及方面将变得清楚。
附图说明
为了更清楚地说明本说明书实施例或现有技术中的技术方案和优点，下面将对实施例或现有技术描述中所需要使用的附图作简单的介绍，显而易见地，下面描述中的附图仅仅是本说明书的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其它附图。
图1示出根据本公开实施例的一种呼吸率检测方法的流程示意图；
图2示出根据本公开实施例的一种呼吸率检测场景的示意图；
图3示出根据本公开实施例的一种目标对象姿态位置调整场景的示意图；
图4示出根据本公开实施例的特征提取方法的流程示意图；
图5示出根据本公开实施例的根据提取到的温度信息确定目标对象呼吸率的流程示意图；
图6示出根据本公开实施例的基于提取到的目标温度信息确定目标对象呼吸率的流程示意图；
图7示出根据本公开实施例的一种呼吸率检测装置的框图；
图8示出根据本公开实施例的一种电子设备的框图；
图9示出根据本公开实施例的另一种电子设备的框图。
具体实施方式
下面将结合本说明书实施例中的附图，对本说明书实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本说明书一部分实施例，而不是全部的实施例。基于本说明书中的实施例，本领域普通技术人员在没有做出创造性劳动的前提下所获得的所有其他实施例，都属于本发明保护的范围。
需要说明的是，本发明的说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的本发明的实施例能够以除了在这里图示或描述的那些以外的顺序实施。此外，术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元的过程、方法、系统、产品或服务器不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。
以下将参考附图详细说明本公开的各种示例性实施例、特征和方面。附图中相同的附图标记表示功能相同或相似的元件。尽管在附图中示出了实施例的各种方面，但是除非特别指出，不必按比例绘制附图。
在这里专用的词“示例性”意为“用作例子、实施例或说明性”。这里作为“示例性”所说明的任何实施例不必解释为优于或好于其它实施例。
本文中术语“和/或”，仅仅是一种描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。另外，本文中术语“至少一种”表示多种中的任意一种或多种中的至少两种的任意组合，例如，包括A、B、C中的至少一种，可以表示包括从A、B和C构成的集合中选择的任意一个或多个元素。
另外，为了更好地说明本公开，在下文的具体实施方式中给出了众多的具体细节。本领域技术人员应当理解，没有某些具体细节，本公开同样可以实施。在一些实例中，对于本领域技术人员熟知的方法、手段、元件和电路未作详细描述，以便于凸显本公开的主旨。
本公开实施例提供一种呼吸率检测方法，该方法可以基于热成像设备拍摄到的热图像中的目标区域中温度的变化情况分析出拍摄对象的呼吸率，从而不需要与拍摄对象产生直接接触即可得到拍摄对象的呼吸率，从而满足人们对于无接触测量呼吸率的客观需求。本公开实施例可以在各种需要进行呼吸率无接触测量的具体场景中被使用，本公开实施例对于该具体场景并不进行具体限定。示例性的，在需要进行隔离的场景、在人流密集的场景，在某些有特殊要求的公共场合等均可以使用本公开实施例提供的方法进行无接触的呼吸率检测。
本公开实施例提供的呼吸率检测方法可以由终端设备、服务器或其它类型的电子设备执行，其中，终端设备可以为用户设备(User Equipment，UE)、移动设备、用户终端、蜂窝电话、无绳电话、个人数字处理(Personal Digital Assistant，PDA)、手持设备、计算设备、车载设备、可穿戴设备等。在一些可能的实现方式中，该呼吸率检测方法可以通过处理器调用存储器中存储的计算机可读指令的方式来实现。下面以电子设备作为执行主体为例对本公开实施例的呼吸率检测方法进行说明。
图1示出根据本公开实施例的一种呼吸率检测方法的流程示意图，如图1所示，上述方法包括：
S101：获取至少两个热图像，上述热图像基于预设区域的温度信息渲染得到，上述预设区域内包括目标对象，并且上述目标对象的呼吸区域落入基于上述预设区域确定的目标区域中。
本公开实施例中的热图像可以由热成像设备成像得到，该热成像设备的拍摄区域即为上述预设区域。在一个实施场景中，可以划分一片区域作为该预设区域，调整热成像设备直至在该热成像设备的视野范围包括该预设区域，比如，该视野范围与该预设区域重合。本公开实施例可以获取拍摄区域内各个位置点的温度信息，并基于该温度信息渲染得到热图像。
本公开实施例根据该热图像中温度的周期性变化规律检测呼吸率，故至少需要两个热图像。本公开实施例旨在测量呼吸率，呼吸率为一项生理参数，上述目标对象相应的为生物体，比如人，后文以进行人呼吸率检测为例进行详述。
本公开实施例并不限定热成像设备的控制方式，其可以响应于预设指令被触发，比如，由控制者或者相关传感器触发相关的控件，该热成像设备即可开启拍摄。在一个实施例中，该热成像设备还可以响应于感应信息被触发，比如，当周围温度升高到预设阈值时，该热成像设备可以自动启动拍照。在另一个实施例中，该热成像设备还可以定时被触发。
进一步地，本公开实施例也不限定热成像设备的拍照模式，比如其拍照帧率，拍照清晰度模式等都可以根据实际情况进行设定。在被触发进行拍照的情况下，该热成像设备可以以视频流形式输出拍摄到的热图像。在一个实施方式中，可以获取视频流，上述视频流中的帧图像基于上述热成像设备拍摄到的温度信息渲染得到。对上述视频流进行显示，并且将上述目标区域标记在上述视频流对应的画面中。
具体地，请参考图2，图2示出根据本公开实施例的一种呼吸率检测场景的示意图。可以在该热成像设备的视野与该预设区域1重合的情况下，由该热成像设备对该预设区域1进行热成像拍摄，输出对应的视频流，该视频流中的每一帧图像均基于该预设区域1的温度信息渲染得到。基于该预设区域1可以确定一关键区域2，在目标对象进入该预设区域1，并且将呼吸区域落入该关键区域2时，该关键区域2的温度变化规律可以反映该目标对象的呼吸率。根据热成像设备的视野与该预设区域1的对应关系，可以唯一确定该关键区域2在热成像设备拍摄到的热图像中对应的区域，该区域即为上述目标区域3。该目标区域3指向帧图像中用于提取温度变化信息的区域，也就是说，通过提取目标区域3的温度信息，分析其变化规律即可确定目标对象的呼吸率。本公开实施例中目标区域可以不止一个，下文以单个目标区域为例进行说明，多个目标区域的情况与单个目标区域的情况基于相同发明构思。
为了便于目标对象及时调整自身位置，可以将上述视频流进行显示，并且在显示画面中标记该目标区域3，以便于目标对象根据显示画面调整姿态位置以确保在该显示画面中该目标对象的呼吸区域落入该目标区域3，本公开中姿态位置的含义为姿态和/或位置。在图2的场景中可以显示出热成像设备对目标对象的拍摄结果，并且显示画面中还标记了目标区域3对应的位置，目标对象可以自行观察。请参考图3，图3示出根据本公开实施例的一种目标对象姿态位置调整场景的示意图。图3左侧目标对象的呼吸区域并未落入目标区域3，则该目标对象可以自行调整姿态位置，直至其自身的呼吸区域落入目标区域3(图3右侧)。在这种情况下，本公开实施例认为目标区域3对应的温度信息变化规律可以准确反映目标对象的呼吸率。
也就是说，本公开实施例可以在上述视频流中提取至少两个目标帧图像，作为步骤S101中的至少两个热图像，上述目标帧图像为上述视频流中满足预设要求的帧图像，上述预设要求为上述目标对象进入上述预设区域并且上述目标对象的呼吸区域落入上述目标帧图像的目标区域。
该目标对象的呼吸区域可以为口鼻区域或口罩区域，该口鼻区域可以被理解为口部区域和/或鼻部区域，在实际应用时，可以将口部区域和鼻部区域分别作为呼吸区域，也可以将口部区域和鼻部区域合并作为一个呼吸区域。
基于上述配置，可以对热成像设备拍摄到的帧图像进行显示，并在显示结果中标记 目标区域，可以使得目标对象能够根据显示的画面配合调整自身姿态位置，以确保在获取到的热图像中，其自身呼吸区域落入目标区域，从而确保后续基于目标区域的温度信息分析得到的呼吸率准确。
在一些可能的实施方式中，还可以监控上述预设区域的温度状态。在上述预设区域的温度状态发生变化的情况下，判断是否存在目标对象进入上述预设区域的情况。若存在，则生成视频流获取指令，上述视频流获取指令用于触发执行上述获取视频流的操作。具体来说，可以通过间歇触发上述热成像设备监控预设区域的温度状态，然后只有在目标对象进入该预设区域的情况下，该热成像设备才输出上述视频流，而目标对象尚未进入该预设区域时，可以不输出上述视频流。热成像设备的间歇拍摄相较于输出上述视频流，资源消耗可以明显降低，也就是说，通过复用热成像设备，使得该热成像设备可以在没有目标对象进入预设区域的情况下间歇启动，而在确定目标对象进行入预设区域的情况下输出视频流，从而达到在无需额外增加硬件成本的前提下，较大限度降低热成像设备的资源消耗的目的。
在一些可能的实施方式中，还可以基于预置传感器监控所述预设区域，在监控结果指示存在目标对象进入所述预设区域的情况下，生成视频流获取指令，所述视频流获取指令用于触发执行所述获取视频流的操作，所述预置传感器包括视觉传感器或感应传感器。本公开实施例并不对视觉传感器进行限制，示例性的，其可以为颜色传感器或灰度传感器。本公开实施例也不对感应传感器进行限制，比如，其可以为红外传感器或微波传感器。通过额外引入视觉传感器或感应传感器，可以低成本对预设区域进行监控，并且只有在目标对象进入预设区域的情况下，才触发上述热成像设备启动拍摄，并输出视频流，从而最大限度降低热成像设备的资源消耗。
本公开实施例中，只有当满足预设要求情况下，对于目标区域的温度分析才可以准确反映呼吸率，该预设要求可以被表达为目标对象进入上述预设区域并且上述目标对象的呼吸区域落入上述目标区域。在本公开的一些实施例中，可以在满足预设要求的情况下，触发执行步骤S101。基于上述配置，可以降低呼吸率检测的启动频率，从而降低呼吸率检测的资源消耗。
在一个实施例中，可以响应于呼吸率检测触发指令，触发执行上述获取至少两个热图像的操作，上述呼吸率检测触发指令用于指示上述预设要求被满足。本公开实施例并不限定该呼吸率检测触发指令的触发方式，可以人工触发也可以机器触发。以图3所示的场景为例，目标对象进入预设区域后可以根据显示画面自行调整位置姿态，当其确定自身的呼吸区域落入该画面的目标区域中后，即可自行或者告知他人触发上述呼吸率检测指令。在另一个实施例中，图3中的画面也可以被显示在其他用户的显示界面(呼吸率检测操控者)中，由呼吸率检测操控者触发该呼吸率检测触发指令。
在另一个实施例中，还可以基于神经网络对上述视频流中的帧图像进行呼吸区域预测，得到呼吸区域预测结果。在上述呼吸区域预测结果与上述目标区域的重合度高于预设阈值的情况下，触发执行上述获取至少两个热图像的操作。本公开实施例并不限定该预设阈值，可以根据实际情况进行自主设定。基于该配置，可以无需人工控制呼吸率检测方法的启动时机，实现全自动的呼吸率检测。
由于本公开实施例中的帧图像是基于温度信息渲染得到的，这种图像伴随着热成像技术和渲染技术的提升而产生，目前对于这种图像的处理方法相对较少，也难以自动从这种图像中提取出充分的信息，因此，较多依赖于人工分析。本公开实施例可以基于神经网络对这种图像进行呼吸区域预测。神经网络在机器学习领域的一种模仿生物神经网络结构和功能的深度学习模型。机器学习(Machine Learning,ML)是一门多领域交叉学科，涉及概率论、统计学、逼近论、凸分析、算法复杂度理论等多门学科。专门研究计算机怎样模拟或实现人类的学习行为，以获取新的知识或技能，重新组织已有的知识结构使之不断改善自身的性能。机器学习是人工智能的核心，是使计算机具有智能的根本途径，其应用遍及人工智能的各个领域。机器学习和深度学习通常包括人工神经网络、置信网络、强化学习、迁移学习、归纳学习、式教学习等技术。深度学习(Deep Learning,DL)是机器学习的分支，是一种试图使用包含复杂结构或由多重非线性变换构成的多个处理层对数据进行高层抽象的算法。
具体地，可以获取样本热图像集和上述样本热图像集中样本图像对应的标签；上述样本热图像基于样本目标对象的温度信息渲染得到，上述标签指向上述样本目标对象的呼吸区域；上述呼吸区域为口鼻区域或口罩区域；对上述样本热图像进行特征提取，得到特征提取结果；根据上述特征提取结果预测呼吸区域，得到呼吸区域预测结果；根据上述呼吸区域预测结果和上述标签，训练上述神经网络。基于上述配置，可以使得训练得到的神经网络具备预测呼吸区域的能力。
本公开实施例并不对于上述训练过程进行详述。示例性的，上述神经网络可以基于特征金字塔逐层进行特征提取，并根据提取到的特征信息预测呼吸区域，根据预测出的呼吸区域与上述标签之间的差异反馈调节该神经网络的参数。由于样本热图像是根据温度信息渲染得到的，其清晰度相较于可见光图像可能有所不及，为了得到充分的具备判别力的特征信息，本公开实施例对于特征提取过程进行优化。
在一个实施例中，请参考图4，其示出根据本公开实施例的特征提取方法的流程示意图。针对样本热图像集中的每张样本热图像，上述特征提取包括：
S1.对样本热图像进行初始特征提取，得到第一特征图。
本公开实施例并不限定初始特征提取的具体方法，示例性的，可以对上述图像进行至少一级的卷积处理，得到上述第一特征图。在进行卷积处理的过程中，可以得到多个不同尺度的图像特征提取结果，可以融合至少两个不同尺度的上述图像特征提取结果得到上述第一特征图。
S2.对第一特征图进行复合特征提取，得到第一特征信息，其中，上述复合特征提取包括通道特征提取。
在一个实施例中，上述对上述第一特征图进行复合特征提取，得到第一特征信息可以包括：对上述第一特征图进行图像特征提取，得到第一提取结果。对上述第一特征图进行通道信息提取，得到第二提取结果。融合上述第一提取结果和上述第二提取结果，得到上述第一特征信息。本公开实施例并不限定对上述第一特征图进行图像特征提取的方法，示例性的，其可以对上述第一特征图进行至少一级卷积处理，得到上述第一提取结果。本公开实施例中的通道信息提取可以关注第一特征图中的各个通道之间的关系的 挖掘。示例性的，其可以基于对多通道的特征进行融合实现。本公开实施例中的复合特征提取可以通过融合上述第一提取结果和上述第二提取结果，既保留第一特征图本身的低阶信息，又可以充分提取到高阶的通道间信息，提升挖掘出的第一特征信息的信息丰富程度和表达力。在实施复合特征提取的过程中，可能用到至少一种融合方法，本公开实施例不对该融合方法进行限定，降维、加法、乘法、内积、卷积、求平均的至少一种及其组合都可以被用于进行融合。
S3.基于上述第一特征信息中的显著特征，对上述第一特征图进行过滤。
本公开实施例中可以根据上述第一特征信息判断上述第一特征图中较为显著的区域和不甚显著的区域，并将较为显著的区域中的信息过滤掉，得到过滤结果。也就是说，第一特征信息包括较为显著的区域和不甚显著的区域，在将较为显著的区域中的信息过滤掉之后，过滤结果中仅包括不甚显著的区域。在一些实施例中，显著特征可以指在第一特征信息中，与生物体(比如，人)的心跳频率吻合程度较高的信号信息。由于第一特征信息中显著特征的分布较为分散，较为显著的区域中70％的信息可能与心跳频率基本吻合，不甚显著的区域中实际上也包括显著特征。本公开实施例并不对显著特征判断方法进行限定，可以基于神经网络也可以基于专家经验进行限定。
S4.提取过滤结果中的第二特征信息。
具体地，可以抑制上述过滤结果中的显著特征，得到第二特征图；上述抑制上述过滤结果中的显著特征，得到第二特征图，包括：对上述过滤结果进行特征提取得到目标特征，对上述目标特征进行复合特征提取得到目标特征信息，以及基于上述目标特征信息中的显著特征，对上述目标特征进行过滤，得到上述第二特征图。在没有达到预设的停止条件(例如，停止条件为第二特征图中的显著特征占比低于5％，又例如，停止条件为第二特征图的更新次数达到预设次数)的情况下，根据上述第二特征图更新上述过滤结果，重复上述抑制上述过滤结果中的显著特征，得到第二特征图的步骤。在达到上述停止条件的情况下，将获取到的每一上述目标特征信息均作为上述第二特征信息。
S5.融合上述第一特征信息和上述第二特征信息，得到上述图像的特征提取结果。
基于上述配置，可以基于层级结构逐层过滤显著特征，并基于过滤结果进行包括通道信息提取的复合特征提取，得到包括多个目标特征信息的第二特征信息，通过逐层挖掘具有判别力的信息，提升第二特征信息的有效度和判别力，进而提升最终的特征提取结果中信息的丰富程度。本公开实施例中该特征提取方法可以用于对样本热图像进行特征提取，在本公开实施例中各需要基于样本热图像训练神经网络的情况下均可以使用。
S102.对于每一上述热图像，提取上述热图像中上述目标区域的温度信息，其中，所述至少两张热图像中目标对象的温度信息跟随目标对象的呼吸呈现周期性变化。
根据前文可知，可以将预设区域中关键区域在热图像中对应的位置确定为预设区域，该预设区域只跟关键区域的位置有关，而与该热图像中各个像素点的位置信息无关，该目标区域是根据该预设区域而被唯一确定的。
在一个实施例中，可以对于每一上述目标帧图像中的目标区域，确定上述目标区域中相关像素点对应的温度信息；根据各上述相关像素点对应的温度信息，计算上述目标 区域对应的温度信息。通过提取目标区域的温度信息，可以进而确定目标对象的呼吸率。
本公开实施例并不限定相关像素点。示例性的，该目标区域中的每一像素点都可以是该相关像素点。在一个实施例中，还可以基于目标区域中每一像素点的温度信息进行像素点过滤，将温度信息不符合预设温度要求的像素点过滤掉，将未被过滤的像素点确定为该相关像素点。本公开实施例并不对预设温度要求进行限定，比如，可以限定温度上限、温度下限或温度区间。
本公开实施例并不限定计算目标区域对应的温度信息的具体方法。示例性的，可以将各相关像素点对应的温度信息的均值或加权均值确定为该目标区域对应的温度信息，本公开实施例对于权值不进行限定，可以由用户根据实际需求进行设定。在一个实施例中，该权值可以与对应的相关像素点距离目标区域的中心位置的距离反相关。示例性的，若相关像素点距离该目标区域的中心位置较近，则该权值较高，若相关像素点距离该目标区域的中心位置距离较远，则该权值较低。
S103.根据提取到的上述温度信息，确定上述目标对象的呼吸率。
本公开实施例认为在目标对象的呼吸区域落入目标区域的情况下，目标对象的呼吸会导致目标区域的温度呈现周期性变化的规律，当目标对象吸气时，目标区域的温度会随之降低，当目标对象呼气时，目标区域的温度会随之升高，通过对提取到的上述温度信息的周期变化规律进行分析，可以确定目标对象的呼吸率。
请参考图5，其示出根据本公开实施例的根据提取到的温度信息确定目标对象呼吸率的流程示意图，包括：
S1031.按照时间顺序对各上述温度信息进行排序，得到温度序列。
在一个实施例中，可以根据获取到的热图像的时序，对热图像进行排序，得到热图像序列，对每个热图像中目标区域的温度信息进行提取，可以得到温度序列。以步骤S101中得到200张热图像为例，每个热图像中均包括目标对象A，则可以得到一个包含200条温度信息的温度序列，该温度序列的温度信息变化规律体现目标对象A的呼吸率。当然，上述预设区域中也可以存在多个目标区域，每个目标区域都可以用于容纳一个目标对象的呼吸区域。也就是说，本公开实施例可以对多个目标对象同时进行呼吸率检测，只需要保证每个目标对象的呼吸区域落入其唯一对应的一个目标区域即可。相应的，在存在多个目标区域的情况下，可以针对每个目标区域，执行上述操作，得到对应的温度序列，进而确定对应于该目标区域的目标对象的呼吸率。
S1032.对上述温度序列进行降噪处理，得到目标温度序列。
本公开实施例中可以确定降噪处理策略和降噪处理方式；根据上述降噪处理策略，基于上述降噪方式对上述温度序列进行处理，得到上述目标温度序列。
本公开实施例并不限定上述降噪处理策略和降噪处理方式的具体内容。示例性的上述降噪处理策略包括下述至少一个：基于高频阈值降噪、基于低频阈值降噪、滤除随机噪声、后验降噪。示例性的，上述降噪处理基于下述至少一种方式实施：独立成分分析、拉普拉斯金字塔、带通滤波、小波、汉明窗。
以后验降噪为例，可以设定后验降噪对应的呼吸率验证条件和降噪经验参数，根据该降噪经验参数对上述温度序列进行降噪，得到目标温度序列。根据该目标温度序列确定目标对象的呼吸率，并基于该呼吸率验证条件对确定出的目标对象的呼吸率进行验证，若验证通过，则确定该降噪经验参数的降噪效果是可以被接受的，在后续再次执行步骤S1032时，可以直接基于该降噪经验参数进行降噪。本公开实施例并不限定降噪经验参数的确定方法，可以根据专家经验得到。
S1033.基于上述目标温度序列，确定上述目标对象的呼吸率。
通过确定温度序列，对该温度序列进行降噪处理，可以滤除影响呼吸率确定的噪声，使得得到的呼吸率更为准确。请参考图6，其示出根据本公开实施例的基于提取到的目标温度信息确定目标对象呼吸率的流程示意图，包括：
S10331.确定上述目标温度序列中多个关键点，上述关键点均为峰值点或均为谷值点。
S10332.对于任意两个相邻关键点，确定上述两个相邻关键点之间时间间隔。
对于提取到的N个关键点，每两个相邻关键点可以计算对应的时间间隔，则可以确定出N-1个时间间隔。
S10333.根据上述时间间隔，确定上述呼吸率。
本公开实施例并不限定根据时间间隔确定上述呼吸率的具体方法。比如，对于上述N-1个时间间隔，可以将其中的一个的倒数确定为上述呼吸率，也可以基于其中的若干时间间隔或全部时间间隔确定呼吸率，比如，可以将上述若干时间间隔或全部时间间隔的平均值的倒数确定为上述呼吸率。本公开实施例通过计算相邻关键点之间的时间间隔，可以准确地确定呼吸率。
本公开实施例提供的呼吸率检测方法，可以在目标对象进入预设区域并且其呼吸区域落入目标区域的情况下，通过对目标区域进行温度信息分析，即可确定目标对象的呼吸率。整个过程不需要与目标对象产生接触，可以被广泛用于各种场景。比如，在医院病房监控中，病人无需佩戴任何设备，即可监控病人的呼吸速率，降低病人的不适感，提高监护病人的质量、效果和效率。在封闭场景下，比如办公室、办公楼大厅，检测在场人员呼吸速率，判断有无异常。在婴儿看护场景下，检测婴儿呼吸，避免婴儿因食物阻塞呼吸道而窒息，及实时分析婴儿呼吸速率以判断婴儿健康状态。在传染风险高的场景下，远程遥控热成像设备拍摄可能成为传染源的目标对象，在避免感染的同时监控目标对象生命体征。
本公开实施例提供的呼吸率检测方法，通过对热成像设备拍摄到的热图像的目标区域的温度信息进行分析，可以确定出目标对象的呼吸率，从而在不接触目标对象的情况下得到呼吸率检测结果，实现了非接触检测，填补了非接触检测场景的空白，并且具备良好的检测速度和检测准确度。
本领域技术人员可以理解，在具体实施方式的上述方法中，各步骤的撰写顺序并不意味着严格地执行顺序而对实施过程构成任何限定，各步骤的具体执行顺序应当以其功能和可能的内在逻辑确定。
可以理解，本公开提及的上述各个方法实施例，在不违背原理逻辑的情况下，均可以彼此相互结合形成结合后的实施例，限于篇幅，本公开不再赘述。
图7示出根据本公开实施例的一种呼吸率检测装置的框图。如图7所示，上述装置包括：
热图像获取模块10，用于获取至少两张热图像，上述至少两张热图像基于预设区域的温度信息渲染得到，上述预设区域内包括目标对象，并且上述目标对象的呼吸区域落入基于上述预设区域确定的目标区域中；
温度信息提取模块20，用于对于上述至少两张热图像中的每一张，提取该热图像中所述目标区域的温度信息，其中，上述至少两张热图像中上述目标对象的温度信息跟随上述目标对象的呼吸呈现周期性变化；
呼吸率确定模块30，用于根据提取到的温度信息，确定上述目标对象的呼吸率。
在一些可能的实施方式中，上述呼吸率检测装置包括热成像设备，上述热成像设备的拍摄区域为上述预设区域，上述装置还包括视频流处理模块，用于获取视频流，上述视频流中的帧图像基于上述热成像设备拍摄到的温度信息渲染得到；对上述视频流进行显示，并且将上述目标区域标记在上述视频流对应的画面中；上述热图像获取模块，用于在上述视频流中提取至少两张目标帧图像，作为上述至少两张热图像，上述至少两张目标帧图像上述视频流中满足预设要求的帧图像，上述预设要求为上述目标对象进入上述预设区域并且上述目标对象的呼吸区域落入上述至少两张目标帧图像的目标区域。
在一些可能的实施方式中，上述装置还包括第一视频流处理触发模块，用于监控上述预设区域的温度状态；在上述预设区域的温度状态发生变化的情况下，判断是否存在目标对象进入上述预设区域的情况；响应于存在所述目标对象进入所述预设区域的情况，则生成视频流获取指令，上述视频流获取指令用于触发执行上述获取视频流的操作。
在一些可能的实施方式中，上述装置还包括第二视频流处理触发模块，用于基于预置传感器监控上述预设区域，在监控结果指示存在目标对象进入上述预设区域的情况下，生成视频流获取指令，上述视频流获取指令用于触发执行上述获取视频流的操作，上述预置传感器包括视觉传感器或感应传感器。
在一些可能的实施方式中，上述装置还包括检测触发模块，用于响应于呼吸率检测触发指令，触发执行上述获取至少两张热图像的操作，上述呼吸率检测触发指令用于指示上述预设要求被满足，上述预设要求为上述目标对象进入上述预设区域并且上述目标对象的呼吸区域落入上述目标区域。
在一些可能的实施方式中，上述装置还包括检测触发模块，用于基于神经网络对上述视频流中的帧图像进行呼吸区域预测，得到呼吸区域预测结果；在上述呼吸区域预测结果与上述目标区域的重合度高于预设阈值的情况下，触发执行上述获取至少两张热图像的操作。
在一些可能的实施方式中，上述神经网络基于下述方法得到获取样本热图像集和上述样本热图像集中多张样本热图像对应的标签；其中，上述多张样本热图像基于样本目标对象的温度信息渲染得到，上述标签指向上述样本目标对象的呼吸区域；上述呼吸 区域为口鼻区域或口罩区域；对上述样本热图像集中的所述多张样本热图像进行特征提取，得到特征提取结果；根据上述特征提取结果预测呼吸区域，得到呼吸区域预测结果；根据上述呼吸区域预测结果和上述标签，训练上述神经网络。
在一些可能的实施方式中，上述装置包括特征提取模块，用于针对每张样本热图像，对该样本热图像进行初始特征提取，得到第一特征图；对该第一特征图进行复合特征提取，得到第一特征信息，其中，该复合特征提取包括通道特征提取；基于该第一特征信息中的显著特征，对该第一特征图进行过滤得到过滤结果；提取该过滤结果中的第二特征信息；融合该第一特征信息和该第二特征信息，得到该样本热图像的特征提取结果。
在一些可能的实施方式中，上述温度信息提取模块，用于对于该热图像中的目标区域，确定上述目标区域中像素点对应的温度信息；根据上述像素点对应的温度信息，计算上述目标区域对应的温度信息。
在一些可能的实施方式中，上述呼吸率确定模块，用于按照时间顺序对上述温度信息进行排序，得到温度序列；对上述温度序列进行降噪处理，得到目标温度序列；基于上述目标温度序列，确定上述目标对象的呼吸率。
在一些可能的实施方式中，上述呼吸率确定模块，用于确定上述目标温度序列中多个关键点，上述关键点均为峰值点或均为谷值点；对于任意两个相邻关键点，确定上述两个相邻关键点之间时间间隔；根据上述时间间隔，确定上述呼吸率。
在一些实施例中，本公开实施例提供的装置具有的功能或包含的模块可以用于执行上文方法实施例描述的方法，其具体实现可以参照上文方法实施例的描述，为了简洁，这里不再赘述。
本公开实施例还提出一种计算机可读存储介质，上述计算机可读存储介质中存储有至少一条指令或至少一段程序，上述至少一条指令或至少一段程序由处理器加载并执行时实现上述方法。计算机可读存储介质可以是非易失性计算机可读存储介质。
本公开实施例还提出一种电子设备，包括：处理器；用于存储处理器可执行指令的存储器；其中，上述处理器被配置为上述方法。
电子设备可以被提供为终端、服务器或其它形态的设备。
图8示出根据本公开实施例的一种电子设备的框图。例如，电子设备800可以是移动电话，计算机，数字广播终端，消息收发设备，游戏控制台，平板设备，医疗设备，健身设备，个人数字助理等终端。
参照图8，电子设备800可以包括以下一个或多个组件：处理组件802，存储器804，电源组件806，多媒体组件808，音频组件810，输入/输出(I/O)的接口812，传感器组件814，以及通信组件816。
处理组件802通常控制电子设备800的整体操作，诸如与显示，电话呼叫，数据通信，相机操作和记录操作相关联的操作。处理组件802可以包括一个或多个处理器820来执行指令，以完成上述的方法的全部或部分步骤。此外，处理组件802可以包括一个 或多个模块，便于处理组件802和其他组件之间的交互。例如，处理组件802可以包括多媒体模块，以方便多媒体组件808和处理组件802之间的交互。
存储器804被配置为存储各种类型的数据以支持在电子设备800的操作。这些数据的示例包括用于在电子设备800上操作的任何应用程序或方法的指令，联系人数据，电话簿数据，消息，图片，视频等。存储器804可以由任何类型的易失性或非易失性存储设备或者它们的组合实现，如静态随机存取存储器(SRAM)，电可擦除可编程只读存储器(EEPROM)，可擦除可编程只读存储器(EPROM)，可编程只读存储器(PROM)，只读存储器(ROM)，磁存储器，快闪存储器，磁盘或光盘。
电源组件806为电子设备800的各种组件提供电力。电源组件806可以包括电源管理系统，一个或多个电源，及其他与为电子设备800生成、管理和分配电力相关联的组件。
多媒体组件808包括在上述电子设备800和用户之间的提供一个输出接口的屏幕。在一些实施例中，屏幕可以包括液晶显示器(LCD)和触摸面板(TP)。如果屏幕包括触摸面板，屏幕可以被实现为触摸屏，以接收来自用户的输入信号。触摸面板包括一个或多个触摸传感器以感测触摸、滑动和触摸面板上的手势。上述触摸传感器可以不仅感测触摸或滑动动作的边界，而且还检测与上述触摸或滑动操作相关的持续时间和压力。在一些实施例中，多媒体组件808包括一个前置摄像头和/或后置摄像头。当电子设备800处于操作模式，如拍摄模式或视频模式时，前置摄像头和/或后置摄像头可以接收外部的多媒体数据。每个前置摄像头和后置摄像头可以是一个固定的光学透镜系统或具有焦距和光学变焦能力。
音频组件810被配置为输出和/或输入音频信号。例如，音频组件810包括一个麦克风(MIC)，当电子设备800处于操作模式，如呼叫模式、记录模式和语音识别模式时，麦克风被配置为接收外部音频信号。所接收的音频信号可以被进一步存储在存储器804或经由通信组件816发送。在一些实施例中，音频组件810还包括一个扬声器，用于输出音频信号。
I/O接口812为处理组件802和外围接口模块之间提供接口，上述外围接口模块可以是键盘，点击轮，按钮等。这些按钮可包括但不限于：主页按钮、音量按钮、启动按钮和锁定按钮。
传感器组件814包括一个或多个传感器，用于为电子设备800提供各个方面的状态评估。例如，传感器组件814可以检测到电子设备800的打开/关闭状态，组件的相对定位，例如上述组件为电子设备800的显示器和小键盘，传感器组件814还可以检测电子设备800或电子设备800一个组件的位置改变，用户与电子设备800接触的存在或不存在，电子设备800方位或加速/减速和电子设备800的温度变化。传感器组件814可以包括接近传感器，被配置用来在没有任何的物理接触时检测附近物体的存在。传感器组件814还可以包括光传感器，如CMOS或CCD图像传感器，用于在成像应用中使用。在一些实施例中，该传感器组件814还可以包括加速度传感器，陀螺仪传感器，磁传感器，压力传感器或温度传感器。
通信组件816被配置为便于电子设备800和其他设备之间有线或无线方式的通信。 电子设备800可以接入基于通信标准的无线网络，如WiFi，2G、3G、4G、5G或它们的组合。在一个示例性实施例中，通信组件816经由广播信道接收来自外部广播管理系统的广播信号或广播相关信息。在一个示例性实施例中，上述通信组件816还包括近场通信(NFC)模块，以促进短程通信。例如，在NFC模块可基于射频识别(RFID)技术，红外数据协会(IrDA)技术，超宽带(UWB)技术，蓝牙(BT)技术和其他技术来实现。
在示例性实施例中，电子设备800可以被一个或多个应用专用集成电路(ASIC)、数字信号处理器(DSP)、数字信号处理设备(DSPD)、可编程逻辑器件(PLD)、现场可编程门阵列(FPGA)、控制器、微控制器、微处理器或其他电子元件实现，用于执行上述方法。
在示例性实施例中，还提供了一种非易失性计算机可读存储介质，例如包括计算机程序指令的存储器804，上述计算机程序指令可由电子设备800的处理器820执行以完成上述方法。
图9示出根据本公开实施例的另一种电子设备的框图。例如，电子设备1900可以被提供为一服务器。参照图9，电子设备1900包括处理组件1922，其进一步包括一个或多个处理器，以及由存储器1932所代表的存储器资源，用于存储可由处理组件1922的执行的指令，例如应用程序。存储器1932中存储的应用程序可以包括一个或一个以上的每一个对应于一组指令的模块。此外，处理组件1922被配置为执行指令，以执行上述方法。
电子设备1900还可以包括一个电源组件1926被配置为执行电子设备1900的电源管理，一个有线或无线网络接口1950被配置为将电子设备1900连接到网络，和一个输入输出(I/O)接口1958。电子设备1900可以操作基于存储在存储器1932的操作系统，例如Windows ServerTM，Mac OS XTM，UnixTM,LinuxTM，FreeBSDTM或类似。
在示例性实施例中，还提供了一种非易失性计算机可读存储介质，例如包括计算机程序指令的存储器1932，上述计算机程序指令可由电子设备1900的处理组件1922执行以完成上述方法。
本公开可以是系统、方法和/或计算机程序产品。计算机程序产品可以包括计算机可读存储介质，其上载有用于使处理器实现本公开的各个方面的计算机可读程序指令。
计算机可读存储介质可以是可以保持和存储由指令执行设备使用的指令的有形设备。计算机可读存储介质例如可以是――但不限于――电存储设备、磁存储设备、光存储设备、电磁存储设备、半导体存储设备或者上述的任意合适的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、静态随机存取存储器(SRAM)、便携式压缩盘只读存储器(CD-ROM)、数字多功能盘(DVD)、记忆棒、软盘、机械编码设备、例如其上存储有指令的打孔卡或凹槽内凸起结构、以及上述的任意合适的组合。这里所使用的计算机可读存储介质不被解释为瞬时信号本身，诸如无线电波或者其他自由传播的电磁波、通过波导或其他传输媒介传播的电磁波(例如，通过光纤电缆的光脉冲)、或者通过电线传输的电信号。
这里所描述的计算机可读程序指令可以从计算机可读存储介质下载到各个计算/处理设备，或者通过网络、例如因特网、局域网、广域网和/或无线网下载到外部计算机或外部存储设备。网络可以包括铜传输电缆、光纤传输、无线传输、路由器、防火墙、交换机、网关计算机和/或边缘服务器。每个计算/处理设备中的网络适配卡或者网络接口从网络接收计算机可读程序指令，并转发该计算机可读程序指令，以供存储在各个计算/处理设备中的计算机可读存储介质中。
用于执行本公开操作的计算机程序指令可以是汇编指令、指令集架构(ISA)指令、机器指令、机器相关指令、微代码、固件指令、状态设置数据、或者以一种或多种编程语言的任意组合编写的源代码或目标代码，上述编程语言包括面向对象的编程语言—诸如Smalltalk、C+等，以及常规的过程式编程语言—诸如“C”语言或类似的编程语言。计算机可读程序指令可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络—包括局域网(LAN)或广域网(WAN)—连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。在一些实施例中，通过利用计算机可读程序指令的状态信息来个性化定制电子电路，例如可编程逻辑电路、现场可编程门阵列(FPGA)或可编程逻辑阵列(PLA)，该电子电路可以执行计算机可读程序指令，从而实现本公开的各个方面。
这里参照根据本公开实施例的方法、装置(系统)和计算机程序产品的流程图和/或框图描述了本公开的各个方面。应当理解，流程图和/或框图的每个方框以及流程图和/或框图中各方框的组合，都可以由计算机可读程序指令实现。
这些计算机可读程序指令可以提供给通用计算机、专用计算机或其它可编程数据处理装置的处理器，从而生产出一种机器，使得这些指令在通过计算机或其它可编程数据处理装置的处理器执行时，产生了实现流程图和/或框图中的一个或多个方框中规定的功能/动作的装置。也可以把这些计算机可读程序指令存储在计算机可读存储介质中，这些指令使得计算机、可编程数据处理装置和/或其他设备以特定方式工作，从而，存储有指令的计算机可读介质则包括一个制造品，其包括实现流程图和/或框图中的一个或多个方框中规定的功能/动作的各个方面的指令。
也可以把计算机可读程序指令加载到计算机、其它可编程数据处理装置、或其它设备上，使得在计算机、其它可编程数据处理装置或其它设备上执行一系列操作步骤，以产生计算机实现的过程，从而使得在计算机、其它可编程数据处理装置、或其它设备上执行的指令实现流程图和/或框图中的一个或多个方框中规定的功能/动作。
附图中的流程图和框图显示了根据本公开的多个实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段或指令的一部分，上述模块、程序段或指令的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。在有些作为替换的实现中，方框中所标注的顺序也可以以不同于附图中所标注的顺序发生。例如，两个连续的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注 意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或动作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
以上已经描述了本公开的各实施例，上述说明是示例性的，并非穷尽性的，并且也不限于所披露的各实施例。在不偏离所说明的各实施例的范围和精神的情况下，对于本技术领域的普通技术人员来说许多修改和变更都是显而易见的。本文中所用术语的选择，旨在最好地解释各实施例的原理、实际应用或对市场中的技术的改进，或者使本技术领域的其它普通技术人员能理解本文披露的各实施例。

Claims (14)

  1. 一种呼吸率检测方法，包括：
    获取至少两张热图像，所述至少两张热图像基于预设区域的温度信息渲染得到，所述预设区域内包括目标对象，并且所述目标对象的呼吸区域落入基于所述预设区域确定的目标区域中；
    对于所述至少两张热图像中的每一张，提取该热图像中目标区域的温度信息，其中，所述至少两张热图像中所述目标对象的温度信息跟随所述目标对象的呼吸呈现周期性变化；
    根据提取到的温度信息，确定所述目标对象的呼吸率。
  2. 根据权利要求1所述的方法，其特征在于，所述呼吸率检测方法应用于呼吸率检测装置，所述呼吸率检测装置包括热成像设备，所述热成像设备的拍摄区域为所述预设区域，所述方法还包括：
    获取视频流，所述视频流中的帧图像基于所述热成像设备拍摄到的温度信息渲染得到；
    对所述视频流进行显示，并且将所述目标区域标记在所述视频流对应的画面中；
    所述获取至少两个热图像，包括：在所述视频流中提取至少两张目标帧图像，作为所述至少两张热图像，所述至少两张目标帧图像为所述视频流中满足预设要求的帧图像，所述预设要求为所述目标对象进入所述预设区域并且所述目标对象的呼吸区域落入所述至少两张目标帧图像的目标区域。
  3. 根据权利要求2所述的方法，其特征在于，所述获取视频流之前，所述方法还包括：
    监控所述预设区域的温度状态；
    在所述预设区域的温度状态发生变化的情况下，判断是否存在目标对象进入所述预设区域的情况；
    响应于存在所述目标对象进入所述预设区域的情况，则生成视频流获取指令，所述视频流获取指令用于触发执行所述获取视频流的操作。
  4. 根据权利要求2所述的方法，其特征在于，所述获取视频流之前，所述方法还包括：
    基于预置传感器监控所述预设区域，在监控结果指示存在目标对象进入所述预设区域的情况下，生成视频流获取指令，所述视频流获取指令用于触发执行所述获取视频流的操作，所述预置传感器包括视觉传感器或感应传感器。
  5. 根据权利要求1至4中任意一项所述的方法，其特征在于，所述方法还包括：
    响应于呼吸率检测触发指令，触发执行所述获取至少两张热图像的操作，所述呼吸率检测触发指令用于指示所述预设要求被满足，所述预设要求为所述目标对象进入所述预设区域并且所述目标对象的呼吸区域落入所述目标区域。
  6. 根据权利要求2至4中任意一项所述的方法，其特征在于，所述方法还包括：
    基于神经网络对所述视频流中的帧图像进行呼吸区域预测，得到呼吸区域预测结果；
    在所述呼吸区域预测结果与所述目标区域的重合度高于预设阈值的情况下，触发执行所述获取至少两张热图像的操作。
  7. 根据权利要求6所述的方法，其特征在于，所述神经网络基于下述方法得到：
    获取样本热图像集和所述样本热图像集中多张样本热图像对应的标签；其中，所述多张样本热图像基于样本目标对象的温度信息渲染得到，所述标签指向所述样本目标对象的呼吸区域；所述呼吸区域为口鼻区域或口罩区域；
    对所述样本热图像集中的所述多张样本热图像进行特征提取，得到特征提取结果；
    根据所述特征提取结果预测呼吸区域，得到呼吸区域预测结果；
    根据所述呼吸区域预测结果和所述标签，训练所述神经网络。
  8. 根据权利要求7所述的方法，其特征在于，所述对所述样本热图像集中的所述多张样本热图像进行特征提取，得到特征提取结果，包括：
    针对每张样本热图像，
    对该样本热图像进行初始特征提取，得到第一特征图；
    对该第一特征图进行复合特征提取，得到第一特征信息，其中，该复合特征提取包括通道特征提取；
    基于该第一特征信息中的显著特征，对该第一特征图进行过滤得到过滤结果；
    提取该过滤结果中的第二特征信息；
    融合该第一特征信息和该第二特征信息，得到该样本热图像的特征提取结果。
  9. 根据权利要求1至8中任意一项所述的方法，其特征在于，所述对于所述至少两张热图像中的每一张，提取该热图像中的目标区域对应的温度信息，包括：
    对于该热图像中的目标区域，确定所述目标区域中像素点对应的温度信息；
    根据所述像素点对应的温度信息，计算所述目标区域对应的温度信息。
  10. 根据权利要求1至9中任意一项所述的方法，其特征在于，所述根据提取到的所述温度信息，确定所述目标对象的呼吸率，包括：
    按照时间顺序对所述温度信息进行排序，得到温度序列；
    对所述温度序列进行降噪处理，得到目标温度序列；
    基于所述目标温度序列，确定所述目标对象的呼吸率。
  11. 根据权利要求10所述的方法，其特征在于，所述基于所述目标温度序列，确定所述目标对象的呼吸率，包括：
    确定所述目标温度序列中多个关键点，所述关键点均为峰值点或均为谷值点；
    对于任意两个相邻关键点，确定所述两个相邻关键点之间时间间隔；
    根据所述时间间隔，确定所述呼吸率。
  12. 一种呼吸率检测装置，包括：
    热图像获取模块，用于获取至少两张热图像，所述至少两张热图像基于预设区域的温度信息渲染得到，所述预设区域内包括目标对象，并且所述目标对象的呼吸区域落入基于所述预设区域确定的目标区域中；
    温度信息提取模块，用于对于所述至少两张热图像中的每一张，提取该热图像中所述目标区域的温度信息，其中，所述至少两张热图像中所述目标对象的温度信息跟随所述目标对象的呼吸呈现周期性变化；
    呼吸率确定模块，用于根据提取到的温度信息，确定所述目标对象的呼吸率。
  13. 一种计算机可读存储介质，所述计算机可读存储介质中存储有至少一条指令或至少一段程序，所述至少一条指令或至少一段程序由处理器加载并执行以实现如权利要求1至11中任意一项所述的呼吸率检测方法。
  14. 一种电子设备，包括至少一个处理器，以及与所述至少一个处理器通信连接的存储器；其中，所述存储器存储有可被所述至少一个处理器执行的指令，所述至少一个处理器通过执行所述存储器存储的指令实现如权利要求1至11中任意一项所述的呼吸率检测方法。
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